1 Private communication for Cyberio do not redistribute KnowHow of UTLN Patents EP2235558/US8638641 [1] and FR1454539 [2] Ales Mishchenko ([email protected], +33662549192), Herve Glotin ([email protected], +33494142824), Pascale Giraudet ([email protected]) October 17, 2014 Motivation. Localisation and identification of bioacoustic sources using pas- sive acoustics is an important task for ecological monitoring.The passive acoustics is particulary important where videomonitoring fails, such as estimating the quantity, biodiversity and habbits of night animals, such as bats, night insects, etc. The most challenging problem in acoustical monitoring remains the localisation and separation of sound sources, robust to echoes and noise. This report presents the solution to this problem, based on the coherence between common signals, captured by differ- ent microphones. This coherence appears because the location and velocity of sound source relative to different microphones correspond to time difference of sound ar- rival to different microphones (TDOA) and difference in sound frequency relative to these microphones (FD). In addition, kinematic relation between velocities and locations entrain relations between TDOAs and FDOAs.
Transcript
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Private communication for Cyberiodo not redistribute
KnowHow of UTLN PatentsEP2235558/US8638641 [1] and FR1454539 [2]
Motivation. Localisation and identification of bioacoustic sources using pas-sive acoustics is an important task for ecological monitoring.The passive acoustics isparticulary important where videomonitoring fails, such as estimating the quantity,biodiversity and habbits of night animals, such as bats, night insects, etc. The mostchallenging problem in acoustical monitoring remains the localisation and separationof sound sources, robust to echoes and noise. This report presents the solution tothis problem, based on the coherence between common signals, captured by differ-ent microphones. This coherence appears because the location and velocity of soundsource relative to different microphones correspond to time difference of sound ar-rival to different microphones (TDOA) and difference in sound frequency relativeto these microphones (FD). In addition, kinematic relation between velocities andlocations entrain relations between TDOAs and FDOAs.
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1 Contents
1.1 The project plan and report overview
see section 2
1.2 Material: Sensors and recordings
see section 3
1.3 Introduction
see section 4
1.4 PATENT1 description: TDOA from 1D signal correla-tion
see section 5
1.5 Transition from PATENT1 to PATENT2 - main patent1corrections
see section 6
1.6 Conclusions of PATENT1
see section 7
1.7 PATENT2 description: The complete algorithm details
see section 8
1. CONTENTS 3
1.8 PATENT2 results: new feature: TDOA correction byTDOA-FD relation
see section 9
1.9 PATENT2 results: assessment of bat orientation, posi-tion and velocity
APPENDIX2: Artificial simulations showing improvements of our meth-ods
APPENDIX3: theory on any number of sensors and Doppler effect
APPENDIX4: Matlab code for simulation
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2 The project plan and report overview
2.1 Studying the state of the art.
Collecting and studying the available bibliography and, in particular, the patentfor whales tracking (Glotin-Giraudet [1]), as well as publications like [12] for patentgeneralisation. Among other state of the art methods put a particular attention tolocalization methods such as [11], [13], [19], [20] and [28]. See section 11.Summary of state of the art. Passive acoustics location systems has been ofconsiderable interest for many years and found wide applications in many areasincluding radar, sonar, and wireless communications [3],[4],[?].It can be applied tosuch various areas of the industry as extraction the trajectory of a fast moving,sound-emitting animals (e.g. bats)? extraction the trajectory of a fast moving,sound-emitting machinery, such as extraction the trajectory of cars before and afteraccident), extraction the airplaines trajectory to identify path deviations and pre-vent the collisions, or detection the enemy’s submarines. Passive acoustics locationsystems usually rely on estimation of Time-Difference of Arrival (TDOA) and/orFrequency Difference of Arrival (FD or FDOA) of a common signals, captured bydifferent microphones. Assuming a narrowband source signal modulating a high-frequency carrier, the Doppler effect (resulting from different velocities relative todifferent microphone) induces negligible time-scaling on the narrowband modulatingsignal, but significantly modifies the carrier frequency, giving rise to the FDOA. TheTDOA accounts for a time-shift in a 1D temporal signal, registered by a microphone.For a stationary emitter, one common technique is to measure the TDOAs of thesource signal to a number of spatially separated receivers. Each TDOA defines ahyperbola in which the emitter must lie. The intersection of the hyperbolae givesthe source location estimate. When the source is moving, FDOA measurementsshould be used in addition to TDOAs to estimate accurately the source positionand velocity. Numerous techniques are available in literature for stationary sourcelocation using TDOAs [?][7]. Relatively little work is available for moving sourcelocation and very few can be identified as combined TDOA+FDOA multiple-sourcemulti-frequency band localisation. Our method is a generalisation of TDOA-basedmethod, described in [1] for the case of TDOA+FDOA localisation.
2. THE PROJECT PLAN AND REPORT OVERVIEW 5
2.2 Adapting algorithms and calculation of TDOA on realdata.
Processing real recordings (57 in general, with particular concentration on the first 7aquired recordings), calculation of TDOA (Time difference of arrival for the recordedsound), smoothing and refinement TDOA(t) with patented coherence condition for-mulas. Including studying and comparision to state of the art approaches to po-sitioning using time-difference of arrival ([21], [40], [42], [46], [52], [62]). See section 5.
2.3 Calculation of all coherence between TDOA and FD.
Developing algorithms and programming the processing of TDOA and FD with gen-eralized patented coherence.Novelty of calculation of all coherence between TDOA and FD. Our tech-nique is based on usage of 2D cross-correlation of Spectrograms/scalograms of differ-ent microphones recordings, resulting in a set of initial simultaneous frequency andtime shift. In our paper we use derivation of FD coherence formulas and gerenralisa-tion of TDOA and FD formulas for the case of arbitrary number of microphones, aswell as combination of TDOA and FD coherence formulas for the case of TDOA+FD.
Usage a shift in frequency of a recorded sound (doppler effect) is a commontechnique to extract the velocity of a fast moving animal/object. Further to thistechnique, we use the refinement the recording data, using an accordance betweensound frequencies registred by different microphones, caused by correspondence be-tween animal/object approach or retreat velocity, relative to different microphones.Similarilty, we use the refinement the recording data, using an accordance betweentime difference of sound arrival and frequency difference of a sound, arrived to differ-ent, caused by coordination between position and velocity of a moving animal/ob-ject. The main part of the algorithm are sets of coherence formulas to refine therecorded data in order to extract velocities and positions coherent between each over.
2.4 Calculation of TDOA and FD on real data and Trackingwith TDOA and FD.
Adapting algorithms and programming the processing 7 real recordings, calcula-tion of TDOA and FD, smoothing TDOA(t) and FD(t), refinement of TDOA with
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patented coherence condition formulas. See section 3.3. Reconstruction of trajec-tory using refined TDOA and FD data.
2.5 Optimization of parameters of algorithms for TDOAcalculation.
Adapt the resulting algorithm of TDOA calculation to a variety of bat recordings,including optimisation of time-step, crosscorrelation-window, number of maximumsand perdentage of TDOA to filter with coherence condition formulas. See section 5.1.
2.6 The complete algorithm (including Patent2) details.
The Cross-correlation of the 3 data types (1D temporal signal, 1D spectrum and 2Dspectrogram/scalogram), followed by data combination in all 3 domains 8.
2.7 Appendix
Estimation of quality of real data and sensors geometry.
Calculation statistical measures (Likehood, Fishes information, Cramer-Rao boundsand other) to analyse data and make conclusions on optimal sensors geometry. Uti-lization and adaptation of an available algorithms, such as [9] and [10]. See section12.
Simplified simulations.
Creating the simplified model of bat tracking with 4 sensors, checking consistencyof coherence equations for FD and TDOA on these simulations. See subsection 4.7.
2. THE PROJECT PLAN AND REPORT OVERVIEW 7
Some theory on using the TDOA and FD for any number of sensors andof using Doppler effect.
The theoretical assessments of using the TDOA and FD for any number of sensors.Advantages of adding the coherence equations for quadruplets, pentaplets, etc tothe coherence equations for triplets.
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Figure 1: The recording(left) and spectrogram(right)of 1st microphone of recordingMyosp
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3 Material: Sensors and recordings
3.1 Geometry of microphones
The geometry of anthena, consisting from 4 recording microphones is showon in afigure 2.
3.2 Real bat sound to be processed.
We are using 57 recordings of real-world bat recordings located at ”/NAS/ALESRES/-DATA”, each containing recording name and time of recording:
’MyospECORx_ 08_16_12_ 22_35_28ALL’ Frequency range in 10kHz units = [4 8]
’PlecotusECORx_ 08_16_12_ 21_40_40ALL’ Frequency range in 10kHz units = [2 6] With cricet = [0.3 6]
’RhinoFerECORx_ 08_16_12_ 22_34_12ALL’ Frequency range in 10kHz units = [7 9] With cricet = [0.3 9]
’RhinohipECORx_ 08_16_12_ 21_58_31ALL’ Frequency range in 10kHz units = [4.2 11.5] With cricet = [0.3 11.5]
’SerotuleECORx_ 08_16_12_ 21_43_32ALL’ Frequency range in 10kHz units = [1.8 6] With cricet = [0.3 6]
’PikECORx_ 08_16_12_ 21_55_44ALL’ Frequency range in 10kHz units = [3.5 6] With cricet = [0.3 6]
’PipECORx_ 08_16_12_ 21_50_36ALL’ Frequency range in 10kHz units = [4 6] With cricet = [0.3 6]
’p6_21_48_27’ Frequency range in 10kHz units = [4 11]
’p6_21_58_43’ Frequency range in 10kHz units = [4 6]
’p6_22_25_20’ Frequency range in 10kHz units = [3 7]
’p6_23_10_03’ Frequency range in 10kHz units = [3 9]
’p7Pkuhl_23_00_25’ Frequency range in 10kHz units = [2.9 6]
’p7Ppip146_56’ Frequency range in 10kHz units = [4 7]
’p7Ppip154_48’ Frequency range in 10kHz units = [4 7]
’p7Ppip228_03’ Frequency range in 10kHz units = [3.5 6]
’p7Ppip333_50’ Frequency range in 10kHz units = [3.5 6]
’p7Rhip201’ Frequency range in 10kHz units = [4 11.5]
3. MATERIAL: SENSORS AND RECORDINGS 9
Figure 2: The geometry of recording microphones
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’p7Rhip218’ Frequency range in 10kHz units = [4 11.5]
’p7Rhip307’ Frequency range in 10kHz units = [4 11.5]
’p7Rhip120’ Frequency range in 10kHz units = [4 11.5]
’h_21_15_21’ Frequency range in 10kHz units = [4 6]
’h_21_21_37’ Frequency range in 10kHz units = [4 11]
’h_21_54_53’ Frequency range in 10kHz units = [2 6]
’h_22_31_54’ Frequency range in 10kHz units = [7 9]
’o_21_39_47’ Frequency range in 10kHz units = [3 9]
’o_21_44_22’ Frequency range in 10kHz units = [3 7]
’o_22_02_56’ Frequency range in 10kHz units = [4 12]
’o_22_25_59’ Frequency range in 10kHz units = [3.8 6]
’sr214804smc4’ Frequency range in 10kHz units =[2.4 5]
’sr215511pikbuzzsmc3’ Frequency range in 10kHz units =[2.4 5]
’sr215545pikpic’ Frequency range in 10kHz units =[2.4 5]
’sr215631smc’ Frequency range in 10kHz units =[2.4 5]
’sf221250’ Frequency range in 10kHz units =[2.4 5]
’sf221533’ Frequency range in 10kHz units =[2.4 5]
’sf222023’ Frequency range in 10kHz units =[2.4 5]
’sf222221’ Frequency range in 10kHz units =[2.4 5]
’sf222816’ Frequency range in 10kHz units =[2.4 5]
’sm214708’ Frequency range in 10kHz units =[2.4 5]
’sm214728’ Frequency range in 10kHz units =[2.4 5]
’sm214941’ Frequency range in 10kHz units =[2.4 5]
’sm215423_2pic1pik’ Frequency range in 10kHz units =[2.4 5]
’sm215856_3sp’ Frequency range in 10kHz units =[2.4 5]
’sm215922_2sp’ Frequency range in 10kHz units =[2.4 5]
’sm221439’ Frequency range in 10kHz units =[2.4 5]
’sg214525pic’ Frequency range in 10kHz units =[2.4 5]
’sg214548smc8’ Frequency range in 10kHz units =[2.4 5]
’sg214653smc9’ Frequency range in 10kHz units =[2.4 5]
’sg214700smc10’ Frequency range in 10kHz units =[2.4 5]
’sg215151pyg’ Frequency range in 10kHz units =[2.4 5]
’sg221333’ Frequency range in 10kHz units =[2.4 5]
’sg221550smc2’ Frequency range in 10kHz units =[2.4 5]
’sg221641smc6’ Frequency range in 10kHz units =[2.4 5]
’sg222003smc5’ Frequency range in 10kHz units =[2.4 5]
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Figure 3: The Spectrogram of 1st microphone of recording Serotule(left upper), Ple-cotus(right upper), Pip(left middle), Pik(right middle), Rhinohip(left lower), Rhi-noFer(right lower)
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3. MATERIAL: SENSORS AND RECORDINGS 11
’sg222141smc7’ Frequency range in 10kHz units =[2.4 5]
’ECOR2116_42’ Frequency range in 10kHz units =[2.4 5]
’ECOR2156_31att312k’ Frequency range in 10kHz units =[2.4 5]
’ECOR2154_58’ Frequency range in 10kHz units =[2.4 5]
We are using mainly 4seconds of 7 first recordings Myosp,Pik,Pip,Plecotus,RhinoFer,Rhinohip,Serotule;recorded by 4microphones each). The corresponding files are located at ”/NAS/ALESRES/-DATA”, each containing recording name (animal name), data and code of recording.These recordings are made with the biggest anthena, shown in a picture 2.
The list of all recordings above contains also the frequency range of a correspond-ing recording in 10kHz units. The recorded signal together with its spectrogram isshown in figure 1 respectively. Other spectrograms recorded with abovementionedanthena are shown in figure 3.
All 57 recordings are made with 6 types of anthenas listed below as coordinatesof each 4 microphones (in meters):
3.3 Recordings and resulting data, arrived to sensors
The recordings description
The available from Cyberio recordings include the folowing.
Flying object. In the following recordings, object is flying in the XYZ space ap-proximately 2-3 meters from the sensors. 7 objects are recorded: Myosp,Pik,Pip,Plecotus,RhinoFer,Rhinohip,Serotule.
Sensors. 4 sensors, placed into small ( 60cm) pyramid.
Recorded sound. In the following recordings, 4 seconds of each objects arerecorded. Approximately 3 seconds in each recordings contain bat-sound.
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The resulting data description
In the previous section, we presented artificial results from the mathematical modelof bat tracking. In this section, we process the real data recordings of 1 flying bat,therefore, from 7 recorded objects (described above), we are presenting ”Myosp”object, corresponding to recording file name
"MyospECORx_ 08_16_12_ 22_35_28"
containing 4 sensors.
4. INTRODUCTION 13
4 Introduction
4.1 The processing: 1D cross-correlation and 2D cross-correlation
TDOA (time difference of arrival) and FD (frequency difference of Doppler effect ofarrived signals) are used in the algorithm below.TDOA can be calculated both from cross-correlation of signal, arrived to differentmicrophones (1D cross-correlation) and from cross-correlation of spectrograms ofrecordings of different microphones (2D cross-correlation). FD is calculated fromcross-correlation of spectrum and using 2D cross-correlation.
2D cross-correlation of spectrograms(described in detailes in later sec-tions)
To detect the difference of times of sound-arrival to each pair of sensors and differ-ence of frequency of arrived sound, the following steps were performed:Calculating Spectrogram. For each sensor the Spectrogram of arrived sound iscalculated.Optional line-detection in Spectrogram. This optional step allows to stressclicks (verical lines in spectrogram), characterisic for some bats. To stress the veri-cal lines, the second-derivative over the horizontal axis is applied over the image ofa spectrogram.Cross-correlation between Spectrograms for TDOA and FD processing.For each pair of sensors, their Spectrograms are cross-correlated in 2D (for TDOAand FD processing), resulting in correlation-images, showing shifts at which Spec-trograms are more similar (best shifts) or more different (worst shifts).Analysis of maximums in Cross-correlation images. Maximums of cross-correlation correspond to estimations for TDOA and FD: The shift in horizontal axis(time), providing maximum of cross-correlation, correspond to the most probableTDOA to these sensors. The shift in vertical axis (frequency), providing maximumof cross-correlation, correspond to the most probable FD in these sensors(frequencyshift because of Doppler effect).
Results for 2D cross-correlation of spectrograms
The resulting functions TDOA(t) and FD(t) show, that, at this stage of process-ing, the Original Spectrogram and Spectrogram with lines detection provide similar
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trends in functions TDOA(t) and FD(t). But as corresponding TDOA(t) and FD(t)are different, it is necessary to keep calculating both variants to see which will bebetter at later, final stages (final trajectory tracking).
As it was noted, the maximums of cross-correlation correspond to estimations forTDOA and FD. Each of the maximums of Cross-correlation correspond to estimationof TDOA (time difference of sound-arrival) and FD (frequency difference of Dopplereffect of arrived signals).In the absence of noise and ideal conditions, the biggest maximum correspond tothe real TDOA and FD. If we perform calculations using biggest maximum, theresulting TDOA(t) and FD(t) will represent very non-smooth curves. Therefore,estimation of trajectory from TDOA(t) and velocity from FD(t) will be also non-smooth and unrealistic. The reason for this is that in the presence of noise the biggestmaximum may not be the ”true-maximum”, corresponding to real TDOA(t) andFD(t). To avoid this, the different combinations of maximums are used to optimizethe smoothness of resulting functions TDOA(t) and FD(t).
1D cross-correlation of signals(described in detailes in this section)
If processing of only TDOA is used, the cross-correlation of signals (instead of spec-trograms) is used. For each pair of sensors, their arrived signals are cross-correlated,resulting in correlation-functions, showing shifts at which signals are more similar(best shifts) or more different (worst shifts).
TDOAs between two microphones are calculated as peaks of correlation ofrecordings of these microphones. Figure 4 shows correlations and peaks of cor-relation of different microphones of Myosp recording.
In the following section we will concentrate on 1D-correlation (1D signal corre-lation, described in our patent1). Later we will describe and combine all types ofcorrelation (1D signal, 1D spectrum, 2D spectrogram, described in our patent2).
4.2 Notes on material from later-described patent 1 (con-clusions on geometry of antena, based on patent1)
Pseudorecordings shown below in section 12 were with 4 sensors made in a wide-spaced grid (10-20m). The real recordings were made with narrow-spaced grid(4sensors, placed into small 60cm-pyramid). As it is seen, wide-spaced grid provide
4. INTRODUCTION 15
Figure 4: Cross-correlation between signals (right) and Peaks of cross-correlation(left)
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good Likelihood function: very highly concentated Likelihood function in case of lowerror spectrogram and suffisiently concentated Likelihood function in case of morerealistic high error spectrogram (see figures in section 12).Simulations of Pseudorecordings on the existing narrow-spaced grid used for realrecordings provide much worse Likelihood function. The resulting Likelihood func-tion (from narrow-spaced grid) are shown in figures in section 12 as well.
Choice of the antenna
As it will be described later, the narrow-grid sensors provide data, which may bedifficult to use for the bat localisation. From the other side, the narrow grids arepreferrable for the algorithm customer (Cyberio).Because of this, the decision wasmade to use the narrow grids and improve the concentation of Likelihood functionby using TDOAs, coherent with each other as well as FDOSs. Despite the fact thatusing the narrow grids is a risk, uising the advance refinement and tracking algo-rithms should improve the ability to use the recorded narrow-grid data to localisethe bat.In the future, we will continue to use narrow grids, as advised by Cyberio becausethe short base offers great logistical and commercial benefits. Our supplement withthe transitivity of FD should allow effective screening of a narrow grids.
Frequencies decay and sound distortion
Another technical difficulty for development of localisation algorithm can be thenatural decay of high frequency components of a sound wave at big distances. Figure5 show the typical decay of sound of different frequencies in air as function of an airhumidity.
Based on theoretical analysis, described in the following sections, as well asgeneral biology and physics of considered system, it is possible to estimate constants,such as range of operation.
Biological constrains estimation
Different theories of functional organisation of cochlea agree in different integationsegments in different frequency-ranges (see figure 6) and this can be used in adjusting
4. INTRODUCTION 17
Figure 5: Frequency dependent attenuation of air (dB) in 30 m distance at differenthumidity (percent)
Figure 6: Anatomical organisation of cochlea, resulting in different sensitivity todifferent frequencies
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Figure 7: Differences in human and bat ear sensitivities
Figure 8: Threshold of hearing of different bats
4. INTRODUCTION 19
the spectrogram according to biological frequency integation principles. Anotheradaptation of the system to biological properties of bat echo-location system isusing different sensibilities to different frequencies (as shown in figure 7).
Physical constrains estimation
From another side, it is possible to use physical constrains of biological echo-locationsystem. For example, the difficulty with location of objects, those sizes is less thansound wavelength. Another physical constrain is different propagation properties ofdifferent frequencies (figure 5). All these biological constraints, optimized by evolu-tion, resulted in a very particular pattern of sound used by bats, as well as frequencydistribution (figure 8). We can use the same constrains to optimize the operation ofour system.
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5 PATENT1 description: TDOA from 1D signal
correlation
Refinement of TDOA of 1D cross-correlation of signals with coherenceformulas
The resulting TDOA can be further refined with patented coherence-condition for-mulas. As in claim 7 of the patent, it is possible to refine the set of TDOA byapplication of the following coherence equations:(4 sensors case)TDOAij + TDOAjk = TDOAik ± errTDOAij + TDOAjh = TDOAih ± errTDOAik + TDOAkh = TDOAih ± errTDOAjk + TDOAkh = TDOAjh ± err
It is possible to fix the allowdable error, as the maximum possible overall systemerror, depending on microphone sensitivity, noise level, sound quality, etc. This waythe equations will serve as a filters out TDOAs which discrepancy is more thanmaximum possible overall system error.
Another approach is to choose the error adaptively, so that a fixed percentageof TDOAs will be allowded to pass thought the filter, whereas others, more worseTDOAs will be filtered out: ±err = ±err(percentage) In the following examples, weutilise the latter approach and choose err(percentage) so that exactly the specifiedpercentage of TDOAs will satisfy the corresponding equation.
For each coherence equation of the form
TDOAij + TDOAjk = TDOAik the error is calculated ERRij = TDOAij +TDOAjk - TDOAik And only a given percentage of a TDOAs is left, providing thelowest ERRij.
Results of refinement of TDOA with coherence formulas
As it can be immediately seen from the 1st(TDOAs without refinement) and 2nd(TDOAswith refinement) pictures of the figure 9, , refinement reduces the amount of smallerroneous TDOAs whereas lefts the amount of big TDOAs the same.
Again, as it can be immediately seen from the 1st(TDOAs without refinement)and 2nd(TDOAs with refinement) pictures of the figure 9, refinement reduces theamount of small erroneous TDOAs whereas lefts the amount of big TDOAs the
5. PATENT1 DESCRIPTION: TDOA FROM 1D SIGNAL CORRELATION 21
Figure 9: Distribution of TDOA, calculated with 200ms-3280ms time-window(Myosp recording): refinement of TDOA (all maximums before and after coher-ence check), histograms count each TDOA one time. In all pictures, the percentageof TDOA left and entropy of histogram (e) is shown above.
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Figure 10: Refinement of TDOA with patented coherence formulas, calculated withshort (15ms) time-window (Myosp recording, at 1sec position). first 2 histogramscount each TDOA one time, last histograms counts each TDOA N times, whereN is a coherent triplets, where this TDOA is presented. This histogram countseach TDOA not one time, but as many times as it is included in different coherenttriplets.
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Figure 11: Entropy of a histogram of all maximums after coherence check, cal-culated 200ms-3280ms time-window (Myosp recording). Legend: ”all maxs” lineshows of entropy before TDOA filtering; ”all cohers” curve shows entropy afterTDOA filtering.
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same. We can also see that the histogram of refined TDOAs is more sparce andcontains more distinct peaks.
Selecting the coherent TDOA those satisfy the coherence equations will allowto leave only correct TDOAs and therefore to produce more smooth TDOA and,therefore, more smooth and correct tracking. Figure 9 shows the process of refine-ment of TDOA with patented coherence formulas. 1st ans 2nd figures represent allmaximums before and after coherence check. The 3rd figure at 10 shows all max-imums after coherence check, counted by number of triplets. Figure 10 shows thesame results, but with short (15ms) time-window. The peaks of a histogram arevisibly separated in this case.
The percentage of TDOA to save after coherence check is based on the En-tropy of a histogram of all maximums after coherence check, is shown at 11 (Myosprecording). It is visible that the entropy drop is happened around 25 percent of
5. PATENT1 DESCRIPTION: TDOA FROM 1D SIGNAL CORRELATION 23
Figure 12: Cross-correlation of signals, arrived to the triplet of microphones withnumbers 1, 2 and 3, Myosp recording. 1st row: signals, 2nd row: spectrograms, 3rdrow: TDOAs between pair of these triplet of microphones (40 percent of the mostcoherent TDOAs are shown), 4th row: Amplitude of recordings
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left-TDOAs. This correspond to high peak-separation in the histogram of all maxi-mums.
Results of refinement of TDOA of 1D cross-correlation of signals
Figure 12 show the results of TDOA refinement with patented coherence-conditionformulas. The 3rd row shows the refined TDOAs between each pair of triplet ofmicrophones.The 3rd row shows the TDOAs refined with the formulas:TDOAij + TDOAjk = TDOAik ±err(40percent)
The following conclusions can be made:1. The refinment results in more smooth and more coherent TDOAs (less false out-bursts of TDOAs, caused by noise in recordings)2. Plotting TDOAs for triplets shows the correctness of coherence of TDOAs (for ex-ample 3 TDOAs mic1-mic2, mic2-mic3, mic3-mic1 illustrate the triplet coherency=0:mic1-mic2 + mic2-mic3 + mic3-mic1 = 0 3. The refinment is the same for allTDOAs-percentage(1percent,5percent,10percent) delivering the same minimum en-tropy value (see figure 11)4. The remaining false outbursts of TDOAs can be, probably removed with usingof FD and FD-TDOA coherence formulas from the patent generalisation.
Figure 12 illustrates also the interesting relation between recording amplitudeand quality of calculated TDOA. It is possible to see the distinction between time-period of a good signal (1sec-2,5sec) and time-period of a mostly noise (before 1secand after 2,5sec). The time-period of a good signal corresponds to high recordingamplitude and high quality of calculated TDOA.
In figure 13 there are summary for Plecotus species for both timeSteps(dt) andtimeWindow of 33ms, tracking 10 maximums and 5 maximums are compared. Ineach image above it is shown (in different subplots) the refined TDOAs and theoriginal signal. Different curves correspond to different pairs of microphones. As itcan be seen, our method allows increasing the number of maximums of correlationfunction, followed by reducing the number of maximums of correlation function bycoherence equations. The resulting number of maximums recinstructs more pointsof TDOA at the cost of increasing the calculation time.
5. PATENT1 DESCRIPTION: TDOA FROM 1D SIGNAL CORRELATION 25
Figure 13: Comparison of 5 biggest maximums for each pair of microphones (left)and 10 biggest maximums for each pair of microphones (right) correlation, 1 percentof refined TDOAs left. Plecotus recordings shown.
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Figure 14: Coherent TDOA, calculated with our method, rambou5-rec2-eco-rx-21-16-42 recording, 40 percents of coherent TDOAs is left.
Comparison of our and Cyberio Refined TDOA for all pair of microphones
On figures 14, 15 the TDOA of rambou5-rec2-eco-rx-21-16-42 recording are pre-sented, acquired, correspondingly, by our and Cyberio’s method. The area of highSNR, where Cyberio’s method was able to reconstruct TDOAs is marked with awave-textured rectangle. It can be seen, that in the areas of small SNR (before0,5sec and after 2sec) our method is still able to calculate some TDOAs, whereasCyberio’s method fails outside the segment of good SNR ratio (before 0,5sec andafter 2sec).
5. PATENT1 DESCRIPTION: TDOA FROM 1D SIGNAL CORRELATION 27
Figure 15: TDOA, calculated with Cyberio’s method, rambou5-rec2-eco-rx-21-16recording.
28
5.1 Optimization of parameters of algorithms for TDOAcalculation
Due to the difference in sounds, emitted by different species, as well as differencein antenae geometry and media of sound-propagation, it is necessary to adapt theresulting algorithm of TDOA calculation to a variety of bat recordings.We optimise time-steps, crosscorrelation-windows, number of maximums and persen-tages of TDOA to filter with coherence condition formulas.
Recordings processed with optimized parameters (expected frequency of click-ing, time-step and time-window) help to improove results. Such as, for example,with expected frequency of clicking is 20hz, time-step and time-window is 25ms isworse than with expected frequency of clicking is 30hz, time-step and time-windowis 16ms.
6. TRANSITION FROMPATENT1 TO PATENT2 - MAIN PATENT1 CORRECTIONS29
6 Transition from PATENT1 to PATENT2 - main
patent1 corrections
6.1 Adding the processing of Frequency Difference of Signal(FD)
If we will add the processing of Frequency Difference of Signal (FD) to this patent,we may do the following:
6.2 In all claims
In all claims which mention TDOA - change TDOA to TDOA and FD and then usecombination of TDOA and FD.
6.3 In Claim 4
CHANGE FROMfrom the position of these peaks, the potential TDOAs corresponding to the prede-termined sounds from the source.TOfrom the horisontal position of these peaks, the potential TDOAs (for further coor-dinates estimation), as well as from the vertical position of these peaks, the potentialFDs (for further velocities estimation) corresponding to the predetermined soundsfrom the source.
6.4 In Claim 7
To claim 7 (if we claim only 4 sensors case)ADD THE FOLLOWING:...selecting the coherent FD... those satisfy the transition principle of the FD andsatisfy the system of equations:FDOSij + FDOSjk = FDOSik ±err(percentage)FDOSij + FDOSjh = FDOSih ±err(percentage)FDOSik + FDOSkh = FDOSih ±err(percentage)FDOSjk + FDOSkh = FDOSjh ±err(percentage)Here we choose err(percentage) so that exactly the specified percentage of TDOAs
30
will satisfy the corresponding equation.Or, better, to claim 7 (if we claim any number of sensors case)ADD THE FOLLOWING:...selecting the coherent TDOA and FD... those satisfy the transition principle ofthe TDOA and FD, as well as satisfy the system of 2n(n-3) equations:
n(n-3) equations:∑
TDOAi,i+1 = TDOAi,m
andn(n-3) equations:
∑FDi,i+1 = FDi,m
where n is a number of sensors 2 ≤ m ≤ N − 2 and summation is over 1 ≤ i ≤ m
6.5 General relation (TDOA and FD) for any number ofsensors
The theorem for the claim 7 in the most general case:any number ofsensors, any type of measurements
Theorem:Let us consider any quality Qi, measured by each sensor Si, 1 ≤ i ≤ n, where n is anumber of sensors. Let dQij be the difference of this quality, measured by sensorsSi and Sj:dQi,j = Qj −Qi
When there are n(n-3) linear conditions on differences dQij of the form :∑dQi,i+1 = dQi,m
where 2 ≤ m ≤ N − 2 and summation is over 1 ≤ i ≤ m
Lemma:The number of sub-polygons of a n-order polygon is n(n-3). This is true for bothconvex and concave polygons, as well as planar and non-planar polygons.Proof of Lemma:Each diagonal of a polygon divides it into 2 sub-polygons. Example: diagonal ACdivides polygon ABCD into sub-polygons ABC and ACDEThe number of diagonals of a polygon is n(n-3)/2. Therefore, the number of sub-polygons of a n-order polygon is n(n-3). See illustration on 16.Lemma is prooved
6. TRANSITION FROMPATENT1 TO PATENT2 - MAIN PATENT1 CORRECTIONS31
Figure 16: Diagonal convex polygon
Figure 17: Diagonal concave polygon
Some examples for the Lemma:This is true also for concave polygons. Example: dotted diagonal divides solid linepolygon into 2 sub-polygons (triangle, containing vertex A and 5-order polygon con-taining vertex B). See illustration on 17.
Examples of non-planar polygons. Example1: Cube on the following pic-ture can be viewed as non-planar polygon 123487651. Diagonal 46 divides polygon123487651 into sub-polygons 1264851 and 7326487. See illustration on 18. Exam-ple2: Octahedron an be viewed as non-planar polygon ABCDEFA. Diagonal CEdivides polygon ABCDEFA into sub-polygons CEABC and CEFDC. See illustra-tion on 19.
Proof of TheoremLet us put all sensors Si, 1 ≤ i ≤ n as vertices of a n-order polygon. As Lemmasays, we will have n(n-3) sub-polygons (chaines of sensors Si...SjSk). For each sub-polygon (each chain) we write the following:∑
dQi,i+1
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Figure 18: Diagonal 3D polygon
Figure 19: Diagonal 3D polygon
6. TRANSITION FROMPATENT1 TO PATENT2 - MAIN PATENT1 CORRECTIONS33
where 2 ≤ m ≤ N − 2 and summation is over 1 ≤ i ≤ mLet us rewrite dQi,j as dQi,j = Qj −Qi:∑
Bat positions: x1, x2, x3, ...Sensors positions: xs1, xs2, xs3, ...Bat movement: xi+1 − xi = vi ∗ δtiwhere δti = ti+1 − ti
Subtracting and adding xs1 results in(xi+1 − xs1)− (xi − xs1) = vi ∗ δtiSubtracting and adding xs2 results in(xi+1 − xs2)− (xi − xs2) = vi ∗ δtiHere (xi+1 − xsj) and (xi − xsj) are distances to sensor sj in moments ti+1 and ticorrespondinglyProjecting on the vectors xi+1 − xsj and xi − xsj results in
|xi+1 − xs1| = |xi − xs1 + δti ∗ Vi,s1
and|xi+1 − xs2| = |xi − xs2 + δti ∗ Vi,s2
where Vi,sj is the velocity of approach to the sensor sj.
Subtracting these equation and using Doppler effect formula, resiults in
c ∗ TDOAi+1,s1s2 = c ∗ TDOAi,s1s2 = δti ∗ FDi,s1s2 ∗ c/freal
orFDi,s1s2
freal=
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7 Conclusions of PATENT1
7.1 Possible future improvements of PATENT1
The reporting patent1 above shows work, devoted to signal analysis in order to un-derstand the relationships between the fundamental measurements which are corre-lation amplitude, FD and TDOA.
Line-detection in spectrograms
Line-detection in spectrogram remains optional. This step allows to stress clicks(verical lines in spectrogram), characterisic for some bats. But, at the stage ofTDOA(t) and FD(t) it is unclear if line-detection improves the performance for thefinal goal (trajectory tracking) or not. TDOA(t) and FD(t) provide similar trendsTDOA(t) and FD(t), but are different. The conclusion is that it is necessary tokeep calculating both variants to see which will be better at later, final stages (finaltrajectory tracking).
Analysis of maximums in Cross-correlation images
Each of the maximums of Cross-correlation correspond to estimation of TDOA (timedifference of sound-arrival) and FD (frequency difference of Doppler effect of arrivedsignals). Analysis of maximums in Cross-correlation images provides better estima-tion in presence of noise. This analysis includes different combinations of maximumsto optimize the smoothness of resulting functions TDOA(t) and FD(t).
Difference in sound of approaching bat and the bat fying away from themicrophones
As we see from the correlation amplitude (see the bottom-left of the 21,20), thesound emitted by approching bat is less coherent than the sound from the flyingaway animal. This may be caused by more complex amplitude modulations of thefrontal direction of the sound. This causes that frontal emmisions are incoherentfrom one microphone to another, which may be due to the head function transfer ofthe bat. As it is visible from 21,20 the time of the nearest approach is around 1,5second.
7. CONCLUSIONS OF PATENT1 35
Figure 20: Amplitudes of recordings, corresponding to all(above) and coher-ent(below) correlation peaks, 1 and 4 microphones, calculated 200ms-3280ms time-window (Myosp recording), 10percents of TDOAs is left.
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Domain of operation and physical relationships between correlation am-plitude and FD, TDOA
The rules for detecting the flying bat with the dFD/dt and dTDOA/dt
As we see from the spectrogram, the typical recordings represent not only the batsound (high frequency bursts between 1 second and 2,5 seconds of recording), butalso the static sound sourses, such as, for example, night insects (low frequencybursts during all the recording) (see spectrogram images above 3). Such static soundsourses provide a very high degree of correlation between pairs of microphones anda very high degree of coherence. Therefore, static sound sourses contribute intothe highest coherent TDOA. In order to remove the static sound sourses from theconsideration, 2 methods can be applied: (1)filtering by frequency (using a frequencyrange for a spectrogram, characteristic for the recorded animals) and (2)using non-zero time-variations of TDOA (dTDOA/dt) and FD (dFD/dt), characteristic forthe moving object like the flying bat.
The rules for detecting the flying bat with the FD
This method includes the following: The FD should be in the following range
|FD(t)| < f · 2 · Vc(f)
, where f is a typical frequency of sound for a given bat (50
kHz), V is the maximum bat velocity (not more than 15 m/s for all the bats) andc is the sound speed (331 m/s). The resulting range is about |FD(t)| < 5kHz
On the top-right of the ?? and ?? the only valuable FD correspond to the timeperiod of sound emmision of the bat (visible on the ??).
7.2 Summary of patent 1
We think that we investigated most of the physical properties of the flight of thebat, so now we can work with the proper TDOA space to localise the bat. This willbe conducted according to the following plan of the nearest steps:
Filtering all TDOA system according to coherence equations and other statisti-cal methods (outliers rejection smoothing, maximum combination, etc)
Introduce simple tracking for the system of coherent TDOA measurement (go fromsingle-point x, v estimation to all-trajectory x(t), v(t) estimation)
7. CONCLUSIONS OF PATENT1 37
Introduce filtering by FD coherence equations (patent extention proposal)
Analyse advantage of tracking (trajectory x(t), v(t) estimation) for the system ofcoherent TDOA and FD
Analyse advantage refinement of TDOA and FD to likehood estimationFor likehood estimation, use more fine gird to have more than 6x6 posible velocitiesand, especially, more than 7x7 spatial positions.Also, for likehood estimation, generate more pseudomeasurements (more than 1piece of spectrogram and more than 1 animals) to have sufficiently large amount ofdata to calculate Likelihood function more accurately.
If simple tracking will be not suffisient, make an Extended Kalman Filtering modelfor the tracking bat system.
Based on the final result (trajectory tracking) advise optimal preprocessing, such asvertical lines detection or other spectrogram processing.
38
8 PATENT2 description: The complete algorithm
details
8.1 The Cross-correlation of the 3 data types (1D temporalsignal, 1D spectrum and 2D spectrogram/scalogram)
The acoustic signal arrived to a microphone represents the 1st data type (1D tempo-ral signal). Calculated 1D spectrum of arrived sound to each microphone representsthe 2nd data type (1D spectrum). The FFT of signal represents the 3rd data type,showing the spectrogram/scalogram of a recorded bat-signal.
Signal-stressing and noise-suppression in Spectrogram/scalogram. Thehunting bat emits high-frequency clicks, appearing as vertical lines in a Spectro-gram/scalogram. Accordingly, line-detection in Spectrogram/scalogram can be per-formed to stress hunting bat sounds and suppress other sounds. This step allowsto stress clicks (verical lines in Spectrogram/scalogram). To stress the verical lines,the second-derivative over the horizontal axis is applied over the image of a Spec-trogram/scalogram.
The Cross-correlation of the 3 data types (1D temporal signal, 1Dspectrum and 2D spectrogram/scalogram)The cross-correlation of the 1st data type (1D temporal signal) is performed foreach pair of sensors (i and j): their signals (SIG(t)i and SIG(t)j) are cross-correlatedin 1D (for TDOA processing), resulting in correlation-function (xcorr(SIGi,SIGj)),showing potential time-shifts at which SIGi and SIGj are more similar (best shifts)or more different (worst shifts).The cross-correlation of the 2nd data type (1D spectrum) is performed for each eachpair of sensors (i and j): their spectrums (FFT(f)i and FFT(f)j) are cross-correlatedin 1D (for FD processing), resulting in correlation-function (xcorr(FFTi,FFTj)),showing frequency-shifts at which FFTi and FFTj are more similar (best frequency-shifts) or more different (worst frequency-shifts).The cross-correlation of the 3rd data type (2D Spectrogram/scalogram) is performedfor each pair of sensors (i and j), their Spectrograms/scalograms (Si and Sj) are cross-correlated in 2D (for TDOA and FD processing), resulting in correlation-images
(xcorr(Si,Sj)), showing 2D shifts (2-component vectors DIJ) at which Si and Sj aremore similar (best shifts) or more different (worst shifts).
8. PATENT2 DESCRIPTION: THE COMPLETE ALGORITHM DETAILS 39
8.2 Analysis of maximums (local peaks) in 3 data-typescross-correlations, resulting in calculation of 3 types ofTDOAs and FDs
Local peaks of cross-correlation (maximums, surrounded by lower values) correspondto estimations for TDOA(Time difference of arrival for the recorded sound) andFD(Frequency difference, caused by the Doppler effect).
Let us denote the maximums (local peaks) in cross-correlation of the 1st datatype (1D temporal signal) as TDOA1, the maximums in cross-correlation of the 2nddata type (1D spectrum) as FD2, whereas the local peaks in cross-correlation ofthe 3rd data type (2D spectrograms/scalograms) as TDOA3 and FD3. In this 3rdcase, the shift in horizontal axis (time), providing maximum of cross-correlation,correspond to the most probable TDOA3 to these sensors. The shift in verticalaxis (frequency), providing maximum of cross-correlation, correspond to the mostprobable FD3 in these sensors(frequency shift because of Doppler effect).
Refinement of TDOA and FD with transitivity constrains (minimi-sation of global error) Selecting the coherent TDOA and FD, those satisfy thetransition principle of the TDOA and FD, formulated as minimisation of the leastmean-square error over 2n(n-3) equations:n(n-3) equations: TDOA(i,i+1) = TDOA(ibeg,iend)andn(n-3) equations: FD(i,i+1) = FD(ibeg,iend)
where n is a number of sensors, 2 ≤ m ≤ n− 2 and summation is over i: 1 ≤ i ≤ mThe TDOAs, FDs, which do not provide the allowadable least mean-square errorover these 2n(n-3) equations are removed from the TDOAs, FDs sets. This is truefor each of 3 types of TDOAs and FDs: TDOA1, FD2, TDOA3 and FD3.
Relation between TDOA and FD The Relation between TDOA and FD(each of 3 types of TDOAs and FDs) can be derived from the physical laws of batmovement.Bat positions: x1, x2, x3, ...Sensors positions: xs1, xs2, xs3, ...Bat movement: xi+1 − xi = vi · δtiwhere δti = ti+1 − tiSubtracting and adding xs1 results in(xi+1 − xs1)− (xi − xs1) = vi · δtiSubtracting and adding xs2 results in
40
(xi+1 − xs2)− (xi − xs2) = vi · δtiHere (xi+1 − xsj) and (xi − xsj) are distances to sensor sj in moments ti+1 and ticorrespondinglyProjecting on the vectors xi+1 − xsj and xi − xsj results inPR(xi+1 − xs1) = PR(xi − xs1) + δti · Vi,s1
andPR(xi+1 − xs2) = PR(xi − xs2) + δti · Vi,s2
where Vi,sj is the velocity of approach to the sensor sj.In other words, the velocity of approach Vi,sj to the sensor sj, multiplies by the timestep ti+1−ti, reults in the difference of distance to the sensor sj in the time momentsti+1 and ti correspondingly (these distances are PR(xi+1 − xs1) and PR(xi − xs1)correspondingly).
Subtracting these equation and using Doppler effect formula, resiults in
The Relation between TDOA and FD is true for each of 3 types of TDOAs andFDs: TDOA1, FD2, TDOA3 and FD3.
Connection between different TDOAs and FDs: TDOA1, FD2, TDOA3and FD3 As we have 3 types of TDOAs and FDs, the transitivity constrains,described in the previous section, take the form:
FDki,s1s2 = ratioFrDti · (TDOAhi+1,s1s2 − TDOAhi,s1s2)where 1 ≤ k ≤ 3, 1 ≤ h ≤ 3, and h = k.
The equality of quantities, received from the different data types result in:TDOA1 = TDOA3andFD2 = FD3 .
The equality above is understood as equality of the sets. These equations, togetherwith transitivity constrains for each data type
8. PATENT2 DESCRIPTION: THE COMPLETE ALGORITHM DETAILS 41
(where k = 1, 3 for TDOAs and k = 2, 3 for FDs), form the complete system ofequations.
These equations interconnect all the 3 datatypes.
8.3 Combination of the transitivity equations into proce-dure of optimization the TDOAs and FDs set
Combination (also named early fusion) of the transitivity equations
Here we choose err(N), depending on the amount N best maximums (TDOAs,FDs) that we plan to leave from the total number of maximums (N TDOAs, N FDs)so that exactly the specified percentage of TDOAs will satisfy the correspondingequation.
Let us take the the 4 sensors case. The system of TD-coherence-equations(TDOA refinment) can be satisfied with some error RTD:
The system of (TDOA1− TDOA3)2 = err(N) can be satisfied with some errorR13:R2
13 =∑
(TDOA1(i1, j1)− TDOA3(i3, j3))2
The distance (TDOA1 − TDOA3)2 is the maximum-set-distance, resulting in theelimination of element from TDOA1 set if it is too far from all the elements ofTDOA3 set.
42
The system of (FD2− FD3)2 = err(N) can be satisfied with some error R23:R2
23 =∑
(FD1(i1, j1)− FD3(i3, j3))2
The distance (FD2− FD3)2 is the maximum-set-distance, reulting in the elimina-tion of element from FD2 set if it is too far from all the elements of FD3 set.
The system of relations TDOAs and FDs can be satisfied with some error RTF :
In order to combine the errors into a single measure of accuracy (perform earlyfusion), we need all errors to have the same units. In our case, R23, R13 and RTD
have units of seconds, where as RFD and RTF have units of Hz. To make bothtypes of variables unitless, it is necessary to divide them either by some character-istic constant, either by their mean value to make them relavtive errors of the formδV alue/V alue instead of absolute errors of the form V alue.
We are using the 1st method of division by characteristic constant ratioFrDtiused in the equation, connecting FD and TDOA:FDki,s1s2 − ratioFrDti · (TDOAhi+1,s1s2 − TDOAhi,s1s2) .In case of the frequency units (RFD and RTF ) we leave them as is, with frequencyunits. In case of the time units (R23, R13 and RTD) we multiply by the ratioFrDti.
Division by characteristic constants provides us errors with the same units:RunitsTD = RTD · ratioFrDti, RunitsFD = RFD, Runits13 = R13 · ratioFrDti,Runits23 = R23 · ratioFrDti, RunitsTF = RTF .Combining the errorsRunitsTD, RunitsFD, Runits13, Runits23, RunitsTF with someweights wTD, wFD, w13, w23, wTF , we have:
The weights wTD, wFD, w13, w23, wTF may be chosen according to the what is moreimportant in the given system of recording and can be optimised to minimize theRunitsTOTAL.In case of treatment of different sound-sources with different frequency, ratioFrDtidepends on the frequency: ratioFrDti = ratioFrDti(FRbat). In this case whileusing combination of errors of the transitivity equations, it is necessary to sum over
8. PATENT2 DESCRIPTION: THE COMPLETE ALGORITHM DETAILS 43
all frequency bands allFRbat:∑
t∈allFRbat RunitsTOTAL(iFRbat), where iFRbat isthe i-th frequency band. The another summation is over all time segments T :∑
t∈T RunitsTOTAL(t).The optimisation of weights wTD, wFD, w13, w23, wTF is done with a constrain wTD+wFD +w13 +w23 +wTF = 1 and is performed to minimise the error RunitsTOTAL insome period T, covering at least one complete call of the animal (for the bat, thiscould be around 2 seconds) .∑
Intersection (also named late fusion) of the transitivity equations An-other, alternative way of simultaneous satisfying the TD-coherence, FD-coherenceequations is their intersection. In this case we satisfy the 3 systems separately withseparate tolerances RTD, RFD:R2
The intersection of these solutions will give us the solution, satisfying all theconditions.
8.4 Equations for reconstruction of missing TDOAs fromFDs
Using the connection between TDOA and FD from the previous section:FDki,s1s2 = ratioFrDti · (TDOAhi+1,s1s2 − TDOAhi,s1s2)and expressing TDOAi+1,s1s2, we have: TDOAi+1,s1s2 = TDOAi,s1s2+FDi,s1s2/ratioFrDtiIt is easy to show also the relation for i − 1: TDOAi−1,s1s2 = TDOAi,s1s2 −FDi,s1s2/ratioFrDti
These equations for TDOAi+1,s1s2 and TDOAi−1,s1s2 from TDOAi,s1s2 and FDi,s1s2
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is true for each of 3 types of TDOAs and FDs: TDOA1, FD2, TDOA3 and FD3.
They allow to reconstruct the missing TDOAs, using the neighboring values ofFD.
In short, the velocity information can be derived from the relation betweenTDOA and FD, shown earlier.This relation has the following formc(fcentral) · TDOAi+1,s1s2 − c(fcentral) · TDOAi,s1s2 = δti · FDi,s1s2 · c(fcentral)/fcentraland, from the previous moment in time c(fcentral)·TDOAi,s1s2−c(fcentral)·TDOAi−1,s1s2 =δti−1 · FDi−1,s1s2 · c(fcentral)/fcentral
Assuming the constant time-step δti−1 = δti = δti+1 and resolving both equa-tions in relation to the c(fcentral) · TDOAi+1,s1s2 and c(fcentral) · TDOAi−1,s1s2 cor-respondingly, we have the following equations:
These equations allow to reconstruct the absent temporal information TDOAi+1,s1s2
and TDOAi−1,s1s2 in case that we have frequency-information FDi+1,s1s2 and FDi−1,s1s2
in the corresponding moment of time.
The section 9 shows the results of reconstruction the absent TDOAs using FD.
8.5 Description of the algorithm flow-chart
1) Calculating Spectrogram/scalogram of arrived sound to each sensor (microphone)
2) Signal-stressing and noise-suppression in Spectrogram/scalogram. For example,hunting bat emits high-frequency clicks, appearing as vertical lines in a Spectro-gram/scalogram. Accordingly, line-detection in Spectrogram/scalogram can be per-formed to stress hunting bat sounds and suppress other sounds. This step allowsto stress clicks (verical lines in Spectrogram/scalogram). To stress the verical lines,the second-derivative over the horizontal axis is applied over the image of a Spec-trogram/scalogram.
3) Cross-correlation between Spectrograms/scalograms for TDOA and FD process-ing. For each pair of sensors, their Spectrograms/scalograms are cross-correlated in2D (for TDOA and FD processing), resulting in correlation-images, showing shifts
8. PATENT2 DESCRIPTION: THE COMPLETE ALGORITHM DETAILS 45
at which Spectrograms/scalograms are more similar (best shifts) or more different(worst shifts).
4) Analysis of maximums in Cross-correlation images, resulting in calculation ofTDOA and FDMaximums of cross-correlation correspond to estimations for TDOA(Timedifference of arrival for the recorded sound) and FD(Frequency difference, causedby the Doppler effect): The shift in horizontal axis (time), providing maximum ofcross-correlation, correspond to the most probable TDOA to these sensors. The shiftin vertical axis (frequency), providing maximum of cross-correlation, correspond tothe most probable FD in these sensors(frequency shift because of Doppler effect).
5) TDOA-algorithm: refinement of TDOA with patented formulas.
Selecting the coherent TDOA, those satisfy the transition principle of the TDOA, aswell as satisfy the system of 3n(n-3) equations: n(n-3) equations: TDOA(i, i+1) =TDOA(ibeg, iend)± errIf these equations are not satisfied with the chosen accuracy err, then correspondingTDOAs are removed from the TDOA sets.For example, in case of 4 microphones the equations become:TDOAij + TDOAjk = TDOAik ± errTDOAij + TDOAjh = TDOAih± errTDOAik + TDOAkh = TDOAih± errTDOAjk + TDOAkh = TDOAjh± err
6) TDOA-FDOA-algorithm: refinement of TDOA and FD with patented formu-las, described in a section .
Selecting the coherent TDOA and FD, those satisfy the transition principle of theTDOA and FD, as well as satisfy the system of 3n(n-3) equations: n(n-3) equations:TDOA(i, i + 1) = TDOA(ibeg, iend) ± err and n(n-3) equations: FD(i, i + 1) =FD(ibeg, iend)±err where n is a number of sensors, 2 ≤ m ≤ n−2 and summationis over i: 1 ≤ i ≤ mIf these equations are not satisfied with the chosen accuracy err, then correspondingTDOAs and FDs are removed from the TDOAs, FDs sets.For example, in case of 4 microphones the equations become:TDOAij + TDOAjk = TDOAik ± errTDOAij + TDOAjh = TDOAih± errTDOAik + TDOAkh = TDOAih± err
Writing these equations for the all 3 data types and adding above-mentioned re-lations between different data types, we have:FDki,s1s2 = ratioFrDti · (TDOAhi+1,s1s2 − TDOAhi,s1s2)± errTDOA1 = TDOA3± errFD2 = FD3± err∑
7) Reconstruction of missing TDOAs from FDs, described in a section .
8) Possible smoothing TDOA(t) and FD(t) using outlier removal process, withthresholds, corresponing to allowable positions and velocity changes.Tracking, reconstruction of trajectory using refined and smoothed TDOA(t) andFD(t) data.
9. PATENT2 RESULTS: NEWFEATURE: TDOA CORRECTION BY TDOA-FD RELATION47
Figure 22: The typical recorded bat-signal (Myop species)
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The Figure 22 shows the typical recorded bat-signal (Myop species). It representsthe 1st data type (1D temporal signal). Calculated 1D spectrum of arrived soundto each sensor (microphone) is shown in the Figure 23 shows the spectrum of thisbat-signal. This is the case of the 2nd data type (1D spectrum), the FFT of signalarrived sound to each sensor (microphone) is performed. Calculating the 3rd datatype, spectrogram/scalogram of arrived sound to each sensor (microphone) is shownin the Figure 24, showing the Spectrogram of a recorded bat-signal, from a figure22.
The figures 25, 26 and 27 show the original TDOA trajectories of recordingsnamed sf221250, sm214708 and sm214708 with missing TDOA points (solid curves),as well as trajectories with adding missing TDOA points using velocity informationfrom all the sources which are consistent with each-other. The sources are spectrum,spectrogram and enhanced spectrogram of abovementioned recordings. It is visible,that, for this example, using enhanced spectrogram is better, since it adds additionalTDOA point at the end of TDOA trajectory.
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Figure 23: The typical spectrum of bat-signal (Myop species)
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9. PATENT2 RESULTS: NEWFEATURE: TDOA CORRECTION BY TDOA-FD RELATION49
Figure 25: The correction of TDOA trajectory with TDOA-FD equation onsf221250 recording. New added TDOAs are shown in dotted lines.
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Figure 26: The correction of TDOA trajectory with TDOA-FD equation onsg215151 recording. New added TDOAs are shown in dotted lines.
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9. PATENT2 RESULTS: NEWFEATURE: TDOA CORRECTION BY TDOA-FD RELATION51
Figure 27: The correction of TDOA trajectory with TDOA-FD equation onsm214708 recording. New added TDOAs are shown in dotted lines.
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The figures show that adding missing TDOA points is sufficiently shooth andthis reconstruction is consistent with the reality. For example, in recording sf221250the recostructed point in the trajectory of TDOA24 is below trajectory, which seemsnot smooth. But in the other trajectories of the triplet (TDOA12, TDOA14) thecorrespondiong point above trajectory, which is consistent with the recostructedpoint.
The figure 27 illustrate the correction of TDOA trajectory with TDOA-FD equa-tion on sm214708 recording (TDOA-FDOA-algorithm).
9. PATENT2 RESULTS: NEWFEATURE: TDOA CORRECTION BY TDOA-FD RELATION53
Figure 28: The state of the art trajectory. The point 5 (shown as empty circle)was not reconstructed by state of the art, it was just interpolated.
9.1 Trajectories, recostructed using the TDOA and FD forany number of sensors - 3D pictures
The figures 28, 29 show reconstructed trajectories using state of the art TDOA andusing TDOA-FD correction.
9.2 Trajectories and time, elapsed by reconstruction, usingstate-of-the-art algorithm and using our TDOA-algorithm(patent1)
The table 1 shows the elapsed time of the solver on all reconstructed points usingstate-of-the-art algorithm and using our TDOA-algorithm (patent 2008).
54
Figure 29: The trajectory, reconstructed using TDOA-FD correction. The point 5was not reconstructed by our algorithm (in difference to state of the art), as well asanother, second additional point 7 new it was reconstructed.
9. PATENT2 RESULTS: NEWFEATURE: TDOA CORRECTION BY TDOA-FD RELATION55
Recording Elapsed time of state-of-the-art algorithm Elapsed time of TDOA-algorithmsg214653 0.15382 0.15062sg221641 0.11541 0.11457sm221439 0.086859 0.085453
Table 1: The elapsed time (seconds) of the state-of-the-art and our TDOA-algorithmon all reconstructed points
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Figure 30: The projection of the trajectories (baseline and our method) on ZX planewith norm of a solution residual of each reconstructed point. Recording O2225.
Recording Residual of baseline Residual of our TDOA-FDOA-algorithmp7Pkuhl 0.039505 0.036843o2225 0.065 0.057728
sf221250 0.050663 0.04023
Table 2: The residual (square norm) of the solver on all reconstructed points
9.3 Trajectories, recostructed using the TDOA and FD (TDOA-FDOA-algorithm) - projection pictures
The figures 30, 31 show different provections of reconstructed trajectories for record-ing O2225, using state-of-the-art algorithm and using our TDOA-FDOA-algorithm(our patent2). It can be seen, that our patent2 not only corrects existing trajectorypoints, but also adds new trajectory points (point 10).
The figures 32 and 33 show provection of reconstructed trajectories for recordingp7Pkuhl and sf221250, using state-of-the-art algorithm and using our TDOA-FDOA-algorithm (our patent2). It can be seen, that our patent2 corrects existing trajectorypoints, making it more smooth.
The table ?? shows the residual of the TDOA-FDOA-algorithm on all recon-structed points.
9. PATENT2 RESULTS: NEWFEATURE: TDOA CORRECTION BY TDOA-FD RELATION57
Figure 31: The projection of the trajectories (baseline and our method) on ZY planewith norm of a solution residual of each reconstructed point. Recording O2225.
Figure 32: The projection of the trajectories (baseline and our method) on YX planewith norm of a solution residual of each reconstructed point. Recording p7Pkuhl.
Figure 33: The projection of the trajectories (baseline and our method) on ZX planewith norm of a solution residual of each reconstructed point. Recording sf221250.
Recording Elapsed time of baseline Elapsed time of our TDOA-FDOA-algorithmp7Pkuhl 0.05005 0.046853o2225 0.037473 0.031836
sf221250 0.1224 0.07658
Table 3: The elapsed time (seconds) of the state-of-the-art and our TDOA-FDOA-algorithm on all reconstructed points
The table ?? shows the elapsed time of the TDOA-FDOA-algorithm on all re-constructed points.
10. PATENT2 RESULTS: ASSESSMENTOF BATORIENTATION, POSITION ANDVELOCITY59
Figure 34: The residual norm in dependance on angle between flight directionand direction to the center of microphones-antenna. The 2 trends are shown: theoverall 2π trend from angle 0(flight towards antenna) to angle 2π(flight away fromantenna) shown in black, as well as partial π trends from angle 0(flight towardsantenna) to angle π(passing antenna) and from angle π(passing antenna) to angle2π((flight away from antenna) shown in grey(red in color version). These trends arein agreement with bats biology on the next picture.
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The figure 34 show the dependance of the residual norm on the angle between flightdirection and direction to the center of microphones-antenna. This dependanceshows maximum of the residual when the microphones are at the side (left, right,up or bottom) of the bat and minimum of the residual when the microphones areat the back or at the front of the bat.This reflects the directionality of bat sound-amplitude. Based on this satistics,one may speculate that the front/back there are maximum/minimum of bat soundamplitude, whereas sides (left, right, up or bottom) are the maximums of gradi-ent of bat sound amplitude. The high gradient of bat sound amplitude leads tohigh difference in sound amplitude, registered by different microphones. The resultis increased difference between recordings of different microphones, leading to high
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Figure 35: The previous experiments on bats sound emmision and bats ear sensi-tivity in dependance on angle between flight direction and direction to the centerof microphones-antenna. The 2 trends are shown: the overall bats ear sensitivitytrend from angle 0(flight towards antenna) to angle 2π(flight away from antenna),as well as partial bats sound emmision trends from angle 0(flight towards antenna)to angle π(passing antenna) and from angle π(passing antenna) to angle 2π((flightaway from antenna). These trends are in agreement with our experiments on theprevious picture.
10. PATENT2 RESULTS: ASSESSMENTOF BATORIENTATION, POSITION ANDVELOCITY61
Figure 36: The residual norm in dependance on distance to the center ofmicrophones-antenna. It is visible, that close-to microphones positions are resolvedmore accurately.
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The figure 34 show 2 trends: the overall increase of residual from angle 0(flighttowards antenna) to angle 2π(flight away from antenna), as well as partial increasefrom angle 0(flight towards antenna) to angle π(passing antenna) and partial de-crease from angle π(passing antenna) to angle 2π((flight away from antenna).These treands are in agreement with measurements of bats sound emmision andsound receiving. Because bats sound emmision shows partial increase from angle0(flight towards antenna) to angle π(passing antenna) and partial decrease fromangle π(passing antenna) to angle 2π((flight away from antenna) - see figure 35.As well as sound receiving shows overall increase of sensitivity from angle 0(flighttowards antenna) to angle 2π(flight away from antenna).
The figure 36 show the dependance of the residual norm on the distance to thecenter of microphones-antenna. This dependance shows 2 regions. The 1st is theregion where distance to different microphones are very different (the region wherethe distance to the center of antenna is not much more than the size of antenna- the 0 to 1 meters distance). The 2nd is the region where distance to differentmicrophones is about the same (2,5 - 4 meters distance). As it may be expected,
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Figure 37: The residual of bat-location in dependance on distance to the center ofmicrophones-antenna. It is visible, that close-to microphones positions are resolvedmore accurately.
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the location in 2nd region is more difficult and results in higher residual.The figure 37 shows the same dependance, but for the residual of bat-location. Asit can be seen, the difference in residuals between 1st and 2nd region is even moreobvious.
The figure 38 show the dependance of the time of trajectory reconstruction onthe distance to the center of microphones-antenna. This dependance shows that ittakes more time to localise bats at the edges (segment between 1 and 2,5 meters) ofthe microphones-antenna.This is probably due to the same reason as figure 34 shows higher residual in regionof high gradient of bat sound amplitude. In other regions, we see that localisationinside the microphones-antenna (0-1 meters) is a little-bit better than localisationfar outside the microphones-antenna (2,5-4,5 meters).
The figures 39 and 40 show the dependance of the frequency of sound, emmitedby bat on the velocity of the bat in our and previous experiments. The previousexperiments on frequency of sound, emmited by bat in dependance on velocity ofthe bat. The clear increase of frequency is visible with increase of velocity.This trend are still not visible in our experiments on the previous picture. This canbe because of different bats species involved. To investigate this effect further, it
10. PATENT2 RESULTS: ASSESSMENTOF BATORIENTATION, POSITION ANDVELOCITY63
Figure 38: The time of trajectory reconstruction in dependance on distance to thecenter of microphones-antenna.
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will be necessary to compensate Doppler effect and different bat species. This willbe one of future research direction.Note, that In difference to clear correlation in angle-effects, this correlation canbe bat-behaviour-dependent, namely: this can be because the bat compensates forDoppler effect only if velocity is outside-generated (in previous experiments - whenbat sits on the pendulum), whereas if velocity is bat-generated (when bat flyes)the bat does not compensate for Doppler effect, caused by this velocity. We willinvesigae this in the future. If this effect will turn to be bat-behaviour-dependent,this will be an interesting finding in bat neurophysiology.
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Figure 39: The frequency of sound, emmited by bat in dependance on velocity ofthe bat. No clear trends are visible, in difference to previous experiments on thenext picture. This can be because of different bats species involved. To investigatethis effect further, it will be necessary to compensate Doppler effect and different batspecies. In difference to clear correlation in angle-effects, this correlation requiresmore investigation and will be one of future research direction.
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10. PATENT2 RESULTS: ASSESSMENTOF BATORIENTATION, POSITION ANDVELOCITY65
Figure 40: The previous experiments on frequency of sound, emmited by batin dependance on velocity of the bat. The clear decrease of frequency is visiblewith increase of velocity (during forward pendulum swing). This trend are still notvisible in our experiments on the previous picture. In difference to clear correlationin angle-effects, this correlation requires more investigation and will be one of futureresearch direction.
66
11 PATENT2 conclusions and future work
This report presents the passive acoustics method capable of localisation of multiplemoving or stationary animals, emmiting sound in different frequency bands. Thismethod is useful forecological monitoring, such as estimating the quantity, biodiver-sity and habbits of night animals, such as bats, night insects, etc.
One of the main point of our future work can be adding the processing of cor-relation amplitude difference and signal amplitude attenuation difference for anynumber of sensors. The attenuation of signal amplitude depends on the distanceto the microphone, as well as on directionality of sound (often corresponding tosituation where animal is facing and approaching microphone or flying backwardsfrom it). This should be in correspondance with calculated velocity (and, there-fore, FDOA) and location (and, therefore, TDOA). Adding the amplitude modalitywill serve as a further refinement of data and more reliable localisation and trajec-tory reconstruction results. Another direction of our future work can be adding theprocessing of phase difference for any number of sensors.
Bibliography
[1] H.Glotin, P.Giraudet, F.Caudal. Real-time robust method for determining thetrajectory of one or more cetaceans by means of passive acoustics, usinga laptop computer, EP2235558 B1,CA2697864A1, EP2235558A1, US8638641,US20100290316, WO2009066012A1, filed 3.09.2008, published 24.04.2013
[2] A.Mishchenko, H.Glotin, P.Giraudet. CONTRAINTES CONJOINTES DETRANSITIVITE DE DIFFERENCES TEMPORELLES ET EFFET DOPLERMULTIBANDES POUR LA SEPARATION, CARACTERISATION, ET LO-CALISATION DE SOURCES SONORES PAR ACOUSTIQUE PASSIVE, filed20.05.2014.
[3] G. C. Carter, Time delay estimation for passive sonar signal processing, IEEETrans. Acoust., Speech, Signal Processing, vol. ASSP-29, pp. 462470, June 1981.
[4] E. Weinstein, Optimal source localization and tracking from passive array mea-surements, IEEE Trans. Acoust., Speech, Signal Processing,vol. ASSP-30, pp.6976, Feb. 1982.
[5] B. Friedlander, A passive localization algorithm and its accuracy analysis, IEEEJ. Ocean Eng., vol. OE-12, pp. 234245, Jan. 1987.
[6] W. W. Smith Jr. and P. G. Steffes, Time delay techniques for satellite interferencelocation system, IEEE Trans. Aerosp. Elect. Syst., vol. 25, pp. 224230, Mar. 1989.
[7] Y. T. Chan and K. C. Ho, A simple and efficient estimator for hyperbolic loca-tion, IEEE Trans. Signal Processing, vol. 42, pp. 19051915, Aug.1994.
[8] C.L.Walters et all. A continental-scale tool for acoustic identification of Europeanbats. Journal of Applied Ecology 2012
67
68 BIBLIOGRAPHY
[9] S. Das. Efficient calculation of Fisher information matrix: Monte Carlo approachusing prior information, Masters thesis, Department of Applied Mathematics andStatistics, The Johns Hopkins University, Baltimore, Maryland, USA, May 2007,http://dspace.library.jhu.edu/handle/1774.2/32459.
[10] J.C.Spall. CramerRao Bounds and Monte Carlo Calculation of the Fisher Infor-mation Matrix in Difficult Problems. Proceeding of the 2004 American ControlConference Boston, Massachusetts June 30 - July 2, 2004
[11] Sheng-Chih Chan, Kun-Chou Lee, Tsung-Nan Lin, Ming-Chung Fang. Under-water positioning by kernel principal component analysis based probabilisticapproach. Applied Acoustics 74 (2013) 11531159
[12] Paolo Annibale and Rudolf Rabenstein. ACOUSTIC SOURCE LOCALIZA-TION AND SPEED ESTIMATION BASED ON TIME-DIFFERENCES-OF-ARRIVAL UNDER TEMPERATURE VARIATIONS. 18th European SignalProcessing Conference (EUSIPCO-2010). Aalborg, Denmark, August 23-27,2010
[13] H. Staras and S. N. Honickman, The accuracy of vehicle location by trilaterationin a dense urban environment, IEEE Transaction on Vehicular Technology, vol.VT-21, no. 1, pp. 3844, February 1972.
[14] J. W. S. Turin. G. L and T. S. Johnston, Simulation of urban vehicle monitoringsystems, IEEE Transaction on Vehicular Technology, vol. VT-21, no. 1, pp. 916,February 1972.
[15] M. Hebert and S. Drulhe, Source localization for distributed robotic teams.Robotics Institute of Carnegie Mellon University, 2002.
[16] B. C. Dalton, Audio-based localisation for ubiquitous sensor networks, Mastersthesis, Massachusetts Institute of Technology, September 2005.
[17] A. J. E. Brandstein. M. S and H. F. Silverman, A closed form location estimatorfor use with room environment microphone arrays, IEEE Transactions on Speechand Audio Processing, vol. 5, no. 1, pp. 4550, 1997.
[18] M. Omologo and P. Svaizer, Acoustic event localization using a crosspower-spectrum phase based technique, Proceedings of IEEE International Conferenceon Acoustics, Speech, and Signal Processing (ICASSP94), vol. 2, pp. 273276,April 1994.
BIBLIOGRAPHY 69
[19] M. M. P. Svaizer and M. Omologo, Acoustic source localization in a three- di-mensional space using crosspower spectrum phase. Proceedings of IEEE Interna-tional Conference on Acoustics, Speech, and Signal Processing (ICASSP97),1997.
[20] M. Omologo and P. Svaizer, Acoustic source localization in noisy and rever-berant environments using csp analysis, Proceedings of IEEE International Con-ference on Acoustics, Speech, and Signal Processing (ICASSP96), pp. 901904,1996.
[21] F. Gustafsson and F. Gunnarsson, Positioning using time-difference of arrivalmea- surements, Proceedings of IEEE International Conference on Acoustics,Speech, and Signal Processing (ICASSP2003), vol. VI, pp. 553556, 2003.
[22] Y. Yu and H. F. Silverman, An improved tdoa-based location estimation algo-rithm for large aperture microphone arrays, Proceedings of IEEE InternationalConference on Acoustics, Speech, and Signal Processing (ICASSP2004), vol. IV,pp. 7780, 2004.
[23] G. D. A. Xuehai Bian, James M. Rehg, Sound source localization in domesticenvi- ronment, GVU center, Georgia Inst. of Technology, 2004.
[24] P. Minero, State of the art on localization and beamforming of an acousticsource, June 21 2004, summary of Localization Techniques.
[25] T. G. Ulrich Klee and J. McDonough, Kalman filters for time delay of arrival-based source localization, EURASIP Journal on Applied Signal Processing, pp.115, 2006.
[26] G. C. Knapp. C. H., Carter, The generalized correlation method for estimationof time delay. IEEE Transactions on Acoustics, Speech and Signal Processing,vol. ASSP-24, no. 4, pp. 320327, August 1976.
[27] G. C. Carter, Coherence and time delay estimation, Proc. IEEE, vol. 75, pp.236 255, February 1991.
[28] J. Benesty, Adaptive eigenvalue decomposition algorithm for passive acousticsource localization, Bell Labs Tech. Memo., 1998.
[29] J. B. Yiteng Huang and G. W. Elko, Adaptive eigenvalue decomposition algo-rithm for realtime acoustic source localization system, IEEE, pp. 937940, 1999.
70 BIBLIOGRAPHY
[30] J. H. DiBiase, A high-accuracy, low-latency technique for talker localization inrever- berant environments using microphone arrays, Ph.D. dissertation, BrownUniversity, May 2000.
[31] M. S. Brandstein, A framework for speech source localization using sen-sor arrays, Ph.D. dissertation, Brown University, Prividence, RI, USA, 1995,ph.D.thesis.
[32] Y. T. Chan, A simple and efficient estimator for hyperbolic location, IEEETrans. on Signal Processing, vol. 42, no. 8, pp. 19051915, August 1994.
[33] Y. T. Chan and K. C. Ho, An efficient closed-form localization solution fromtime difference of arrival measurements, IEEE, 1994.
[34] J. C. Schau and A. Z. Robinson, Passive source localization employing inter-secting spherical surfaces from time-of-arrival differences, IEEE Transactions onAcoustic, Speech, and Signal Processing, vol. 29, no. 4, pp. 984995, July 1989.
[35] J. O. Smith and J. S. Abel, Closed-form least-squares source location estima-tion from range-difference measurements, IEEE Trans. Acoust., Speech, SignalProcess- ing, vol. ASSP-35, no. 12, pp. 16611669, December 1987.
[36] J. S. Abel and J. O. Smith, The spherical interpolation method for closed-form passive localization using range difference measurements, Proc. ICASSP-87(Dallas, TX), pp. 471474, 1987.
[37] J. O. Smith and J. S. Abel, The spherical interpolation method for sourcelocaliza- tion, IEEE Journal of Oceanic Engineering, vol. OE-12, no. 1, pp.246252, January 1987.
[38] J. S. Abel and J. O. Smith, Source range and depth estimation from multipathrange difference measurements, IEEE Transaction on Acoustic, Speech, and Sig-nal Processing, vol. 37, no. 8, pp. 11571165, August 1989.
[39] B. M. H. Rabinkin. D. V, A dsp implementation of source location using mi-crophone arrays. Journal of Acoustical Society of America, vol. 99, no. 4, April1996.
[40] EKSLER, Time delay estimate method for detection of acoustic source location,in In Proceedings of 9th Conference and Competition STUDENT EEICT, vol.2. FEKT, 2003, pp. 216220.
BIBLIOGRAPHY 71
[41] P. A. G. Bendat. J. S., Engineering Applications of Correlation and SpectralAnalysis. John Wiley and Sons, Inc., 1980.
[42] G. C. Carter, Time delay estimation for passive sonar signal processing, IEEETrans. Acoust., Speech, Signal Processing, vol. ASSP-29, no. 3, pp. 463470, June1981.
[43] B. T. Fang, Simple solutions for hyperbolic and related position fixes, IEEETrans. on Aerosp. Electron. Systems, vol. 26, no. 5, pp. 748753, September 1990.
[44] W. H. Foy, Position-location solutions by taylor-series estimation, IEEE Trans.Aerosp. Electron. Systems, vol. AES-12, no. 12, pp. 187194, March 1976.
[45] P. Mailloux, Phased Array Antenna Handbook. Artech House, 1994.
[46] D. Carevic, Tracking target in cluttered environment using multilateral time-delay measurements. The Journal of the Acoustical Society of America, 2003.
[47] C. L. G. Ferguson. B. G and L. K. W, Locating far-field impulsive sound sourcesin air by triangulation, Journal of Acoustical Society of America, 2002.
[48] R. O. Schmidt, A new approach to geometry of range difference location,IEEE Transactions on Aerospace and Electronic Systems, vol. AES-8, no. 6,pp. 821835, November 1972.
[49] G. W. E. Yiteng Huang, Jacob Benesty and R. M. Mersereau, Real-time pas-sive source localization: A practical linear-correction least squares approach.IEEE Transactions on Speech and Audio Processing, vol. 9, no. 8, November2001.
[50] J. S. Abel, A divide and conquer approach to least-squares estimation, IEEETrans- action on Aerospace and Electronic Systems, vol. 26, pp. 423427, March1990.
[51] J. B. Yiteng Huang and G. W. Elko, Passive acoustic source localiza-tion for video camera steering, Acoustics, Speech, and Signal Processing,2000.ICASSP00.Proceedings, vol. 2, 2000.
[52] W. R. Hahn and S. A. Tretter, Optimum processing for delay-vector estimationin passive signal arrays. IEEE Transaction in Information Theory, 1973.
72 BIBLIOGRAPHY
[53] W. K. M. K. W. Cheung, H. C. So and Y. T. Chan, Least squares algorithms fortime-of-arrival-based mobile location, IEEE Transactions on Signal Processing,vol. 52, no. 4, pp. 11211128, April 2004.
[54] S. M. S. Bechler. D and K. Kroschel, System for robust 3d speaker trackingusing microphone array measurements, in Proc. 2004 IEEE/RSJ InternationalConference on Intelligent Robots and Systems, Sep 28 - Oct 2 2004, pp. 21172122.
[55] H. C. So and S. P. Hui, Constrained location algorithm using tdoa measure-ments, IEICE Trans. Fundamentals, vol. E86-A, no. 12, December 2003.
[56] W. R. P. H. F. Silverman and J. L. Flanagan., The huge microphone array(hma)- part i. IEEE Transactions on Concurrency, vol. 6, no. 4, pp. 3646,October- December 1998.
[57] , The huge microphone array (hma)-part ii. IEEE Transactions on Concur-rency, vol. 7, no. 1, pp. 3247, January-March 1999.
[58] A. A. Eugene Weinstein, Kenneth Steele and J. Glass, Loud: A 1020-nodemodu- lar microphone array and beamformer for intelligent computing spaces,MIT/LCS Technical Memo, 2004.
[59] CMOS Analog Multiplexers/Demultiplexers, Texas Instruments, June 2003,data Sheet.
[60] W. T. V. W. H. Press, S. A. Teukolsky and B. P. Flannery, Numerical Recipesin C. Cambridge University Press, New York, 1992.
[61] B. Friedlander, A passive localization algorithm and its accuracy analysis, IEEEJ. Ocean Eng., vol. OE-12, pp. 234245, January 1987.
[62] Robert MacCurdy, Rich Gabrielson, Eric Spaulding, Alejandro Purgue,Kathryn Cortopassi. Automatic Animal Tracking Using Matched Filters andTime Difference of Arrival. JOURNAL OF COMMUNICATIONS, VOL. 4, NO.7, AUGUST 2009
[63] Cynthia F. Moss, Chen Chiu and Annemarie Surlykke. Adaptive vocal behav-ior drives perception by echolocation in bats. Current Opinion in Neurobiology,2011,21:645652(pdf)
BIBLIOGRAPHY 73
[64] Yossi Yovel, Ben Falk, Cynthia F. Moss, Nachum Ulanovsky. Active Controlof Acoustic Field-of-View in a Biosonar System. PLoS Biology, September 2011,Volume 9, Issue 9, e1001150(pdf)
74 BIBLIOGRAPHY
12 APPENDIX1: Statistics (Likehood, Fisher in-
formation, CramerRao bound)
12.1 The method of using statistical measures to analysedata
To analyse real-world and simulated data, it is possible to use different statisticalmeasures to analyse the amount of useful information in measured data ([9], [10]).
The likelihood of the set of parameter values given the observed outcomes is theprobability of some observed outcomes given a set of parameter values.
In our case, the parameter values are unobservable coordinates and velocitiers ofthe bat, while observed outcomes are raw sound or preprocessed sound recordings.It is convinient to use spectrogram of a recorded sound as observed outcome. Tocalculate the likelihood it is necessary to have a statistically significant number ofrecordings with corresponding bat trajectory (with corresponding coordinates andvelocitiers of the bat). In reality, we do not have trajectories and the number ofavailable recordings is very small. However, it is possible to create artificial spectro-grams to create artificial likelihood function. This will not describe the quality of asystem, but can be used to analyse some papameters of the recording system, suchas the distance between microphones.
The Fisher information ([9]) is a way of measuring the amount of informationthat an observable spectrograms carry about an unknown coordinates and velocitiersupon which the probability of spectrograms depends.
CramerRao bound (CRB [10]) states that the variance of any unbiased estima-tor θ of unobservable parameters θ is at least as high as the inverse of the Fisherinformation I(θ).In our case, Fisher information Matrix is to estimate the unobservable coordinatesand velocitiers (parameters θ are v(t1), v(t2), ..., v(tn), x(t1), x(t2), ..., x(tn)) from aset of observed data set (spectrograms Si(tj), where 1 ≤ i ≤ Nsensors,1 ≤ j ≤NTimeMoments).The Fisher information measures the amount of information that an observable ran-dom variable Si(tj) carries about an unknown parameters x(tj), v(tj). To calculateFisher information Matrix it is necessary to calculate the likelihood function (prob-ability density) of Si(tj) conditional on the value of x(tj), v(tj).For each time moment tj, 1 ≤ j ≤ NTimeMoments, we have to estimate unknown co-
ordinate and velocity x, v from observed spectrograms Si, 1 ≤ i ≤ Nsensors
Figure 41: The scheme of calculating Fisher information (FIM) and CramerRaobound (CRB), from [10]
Likelihood function in our case takes the form: l(x, v|Si). The log-likelihood func-tion is the natural logarithm:L(x, v|Si) = ln(l(x, v|Si))To calculate Fisher information let us define the gradient vector g
g(x, v|Si) =dL(x,v|Si)
d(x;v)= dL(θ|Si)
dθ
and the Hessian matrixH(x, v|Si) =
d2L(x,v|Si)d(x;v)d(x;v)T
= dL(θ|Si)dθ
When the Fisher information matrix (F (θ) = F (x, v)) takes the form:F (x, v) = E[g(x, v|Si) · gT (x, v|Si)|x, v] = −E[H(x, v|Si)|x, v]
As it was mentioned above, we do not have trajectories for available recordings.In this conditions, we can not use statistical measures (Likelihood, Fisher informa-tion, CramerRao bound) to describe the quality of our recording system. However,we can use Likelihood function to analyse which distance between microphones pro-vide good localisation for a given properties of sound (spectral properties, soundvelocity and typical distance to the bat). This will be decribed in details below.
12.2 Notes on material from later-described patent 1 (con-clusions on geometry of antena, based on patent1)
Pseudorecordings shown above were with 4 sensors made in a wide-spaced grid (10-20m). The real recordings were made with narrow-spaced grid(4 sensors, placed
76 BIBLIOGRAPHY
Figure 42: 2m-grid Likelihood function of coordinates, resulting from low errorspectrogram (Myosp recording). Each line consists from 4 spectrograms, receivedby 4 sensors, followed by resulting likelihood function (coordinates estimation). It isvisible, that likelihood function is not very concentrated - it is not easy to determinewhere the bat is likely to be at this moment. Corresponding results with wide-spacedgrid, shown in previous sections, were better.
Figure 43: 2m-grid Likelihood function of velocities, resulting from low error spec-trogram (Myosp recording). Each line consists from 4 spectrograms, received by 4
sensors, followed by resulting likelihood function (velocities V = Vx, Vy estimation).Wide-spaced grid was better.
78 BIBLIOGRAPHY
Figure 44: 60cm-grid Likelihood function of coordinates, resulting from high er-ror spectrogram (Myosp recording). It is visible, that likelihood function is verywidespread - it is impossible to determine where the bat is likely to be at this mo-ment. Results with wide-spaced grid, shown in previous sections, were much better.
Figure 45: 60cm-grid Likelihood function of velocities, resulting from high errorspectrogram (Myosp recording). Each line consists from 4 spectrograms, received by
4 sensors, followed by resulting likelihood function (velocities V = Vx, Vy estimation).Corresponding results with wide-spaced grid, shown in previous sections, were muchbetter.
80 BIBLIOGRAPHY
into small 60cm-pyramid). As it is seen from above, wide-spaced grid provide goodLikelihood function: very highly concentated Likelihood function in case of low errorspectrogram (figures 48,49) and suffisiently concentated Likelihood function in caseof more realistic high error spectrogram (figures 50,51).Simulations of Pseudorecordings on the existing narrow-spaced grid used for realrecordings provide much worse Likelihood function. The resulting Likelihood func-tion (from narrow-spaced grid) are shown in the following figures 42,43, 44,45.
13. APPENDIX2: ARTIFICIAL SIMULATIONS SHOWING IMPROVEMENTS OF OURMETHODS81
13 APPENDIX2: Artificial simulations showing
improvements of our methods
13.1 The numerical demonstration of the proposed claims
Based on the physics of sound, acoustical properties (properties of emmitted sounds),physical properties (natural bounds and statistical properties of velocity and trajec-tory of flying animals), the model of bat tracking includes the folowing.
Flying object. In the following simulations, object is flying in the XY plane(for better illustration).
Sensors. Here 4 virtual sensors were used, placed at the 4 angles of a squarein the XY plane.
Emitting sound. In the following results, transformations of real-world batsound were used. Transformation includes frequency-shifts, time-shifts, croppingand glueing of parts, addition of noise. The detailed procedure of transfomation isas follows: The real-world bat sound (figure ??) is transformed into the spectrogram(figure 1), which is divided into subperiods (subspectrograms), those size correspondto typical distance between bat clicks. These subspectrograms are transformed dif-ferently for 4 different sensors. These different transformations include horisontalshift to simulate TDOA from the current bat-position to different sensors, as wellas vertical shift to simulate FD (Doppler effect) according to current bat-velocitywith respect to different sensors. This detailed procedure is used to estimate thelikehood function (see sections below). The simplified procedure of transfomationinclude just the information about TDOA and FD, without any information of realemitted sound. This simplified procedure is used to simulate the trajectory, as wellas TDOA and FD, corresponding to positions and velocities.
Results of simulation
Results of simulation with 4 sensors, placed at the angles of a square, are shownon the pictures (figures ??,??,??). These pictures show the simulation of the flying
82 BIBLIOGRAPHY
object (the bat), starting the movement from the center (marked by cross) andperforming stohastic motion near existing 4 sensors (shown at the corners of thepicture).The flying object (bat) constantly emits sound, whose propagation is simulated bytaking into account TDOA and FD, corresponding to positions and velocities of thebat. The transformed by TDOA and FD sound is registered by 4 sensors.The difference of time of sound-arrival to each pair of sensors is calculated accordingpositions of the bat and is shown in-between each pair of sensors (TDOA in ms).Also, nearby to TDOA, the difference of frequency of sound, registered by each pairof sensors is shown (FD in Hz).In the middle of the picture, the sum of all 4 TDOAs and FDODs are shown. Itis visible that both sums are close to zero, which supports the theoretical proofthat sum of TDOAs=0 (used for TDOA selector in Glotin-Giraudet [1]), as well assupports the theoretical proof that sum of FDODs=0 (shown on a separate page).
Generating pseudomeasurements
Calculation of Likelihood function L(x, v and subsequent Fisher information matrixF (x, v) is possible either analytically or using large amount of data.To calculate L(x, v and subsequently F (x, v) by Monte Carlo methods, many record-ings are necessary. As we have only 7 recordings by 4 microphones, it is necessaryto use mathematical model to generate large amount of data.Generating pseudomeasurements is done with mathematical model described earlier.Flying object is described in a single point of trajectory in the XY plane. 4 sensorswere used, placed at the angles of a square in the XY plane. Object (bat) is situatedinside this square, divided into 7x7 grid. Velocities of the object are measured alsoonto the 2D grid, corresponding to Vx, Vy having 6x6 nodes of measurements.Emitting sound in the following results are made by transformation of the real-worldbat sound (frequency-shifts, time-shifts, cropping and glueing of parts, addition ofnoise). The detailed procedure of transfomation is as follows: The real-world batsound (figure ??) is transformed into the spectrogram (figure 1), which is dividedinto subperiods (subspectrograms), those size correspond to typical distance be-tween bat clicks. These subspectrograms are transformed differently for 4 differentsensors. These different transformations include horisontal shift to simulate TDOAfrom the current bat-position to different sensors, as well as vertical shift to simu-late FD (Doppler effect) according to current bat-velocity with respect to differentsensors.
13. APPENDIX2: ARTIFICIAL SIMULATIONS SHOWING IMPROVEMENTS OF OURMETHODS83
Figure 46: Spectrograms with high error potential of sensor1 pseudorecordings.Choice of 4 spectrograms from this set determines pseudomeasurements of 4 sensorsand, therefore, estimation of hidden parameters x, v
Figure 47: Spectrograms with low error potential of sensor1 pseudorecordings.Choice of 4 spectrograms from this set determines pseudomeasurements of 4 sensorsand, therefore, estimation of hidden parameters x, v
The resulting spectrograms are shown in the following figures.
In figures 46,47 two variants of transformation of the real-world bat sound isused. Figure 47 shows frequency-shift and time-shift, surrounded by noise. Thisprovides the lowest possible error and most ”concentrated” likehood function. Fig-ure 46 shows frequency-shift and time-shift, surrounded by similar patterns. Thisprovides the highest possible error and most ”wide-spread” likehood function. Thelatter pseudomeasurements are more close to real measurements.
Calculation the likehood function
The first step to estimate parameters x, v is seeking and estimating the likelihoodby Monte Carlo methods. In our case (complicated statistical model) likelihoodfunction can only be approximated by Monte Carlo methods using generated pseu-domeasurements.The resulting likelihood functions are shown in the following figures 48,49,50,51.
84 BIBLIOGRAPHY
Figure 48: Likelihood function of coordinates, resulting from low error spectrogram(Myosp recording). Each line consists from 4 spectrograms, received by 4 sensors,followed by resulting likelihood function (coordinates estimation). It is visible, thatlikelihood function has big maximum (black spot), where the bat is likely to be atthis moment
13. APPENDIX2: ARTIFICIAL SIMULATIONS SHOWING IMPROVEMENTS OF OURMETHODS85
Figure 49: Likelihood function of velocities, resulting from low error spectrogram(Myosp recording). Each line consists from 4 spectrograms, received by 4 sensors,
followed by resulting likelihood function (velocities V = Vx, Vy estimation). It isvisible, that likelihood function has big maximum (black spot), corresponding to
the velocity V = Vx, Vy of the bat.
86 BIBLIOGRAPHY
Figure 50: Likelihood function of coordinates, resulting from high error spectrogram(Myosp recording). Each line consists from 4 spectrograms, received by 4 sensors,followed by resulting likelihood function (coordinates estimation)
13. APPENDIX2: ARTIFICIAL SIMULATIONS SHOWING IMPROVEMENTS OF OURMETHODS87
Figure 51: Likelihood function of velocities, resulting from high error spectrogram(Myosp recording). Each line consists from 4 spectrograms, received by 4 sensors,
followed by resulting likelihood function (velocities V = Vx, Vy estimation)
88 BIBLIOGRAPHY
14 APPENDIX3: theory on any number of sen-
sors and Doppler effect
Due to the difference in sounds, emitted by different species, as well as differencein antenae geometry and media of sound-propagation, it is necessary to adapt theresulting algorithm of TDOA calculation to a variety of bat recordings.In this section we optimise time-steps, crosscorrelation-windows, number of maxi-mums and persentages of TDOA to filter with coherence condition formulas.
14.1 The theoretical assessments of using the TDOA andFD for any number of sensors
Advantages of adding the coherence equations for quadruplets, penta-plets, etc to the coherence equations for triplets
With N microphones (1 < i, j < N), we have N(N − 1)/2 pairs(p) of mi-
crophones, (N(N−1)/2)!6(N(N−1)/2−3)!
triplets(p3) of pairs of microphones and (N(N−1)/2)!24(N(N−1)/2−4)!
quadruplets(p4) of pairs of microphones. Here the number of pairs p correspondto the number of unknowns (number of TDOAs, for example), whereas the numberof triplets p3 correspond to the number of coherence equations.
The number of pairs grow approximately as a square of number of microphones.For the number of microphones from 3 to 10, number of pairs are, correspondingly3,6,10,15,21,28,36,45. However, the number of triplets grow much faster than thenumber of pairs. The number of quadruplets grow even more faster.As the number of pairs correspond to the number of unknown TDOAs, whereas thenumber of triplets and quadruplets correspond to the number of coherence equa-tions, the more triplets and quadruplets we have, the more number of correlation-maximums we can process. On 52 there are possible number of maximums in case oftriplets and quadruplets. Note, that these are the maximum number of maximums- if the number of maximums is smaller, we have less unknowns then equations -normal situation if we try to satisfy each coherence equation not exactly, but withsome tolerance.
The theorem in the most general case:any number of sensors, any typeof measurements
Theorem:Let us consider any quality Qi, measured by each sensor Si, 1 ≤ i ≤ n, where n is a
Figure 52: The maximum number of maximums, able to process in case of tripletsand quadruplets for 4,5,6,7 microphones(X-axis)
4 4.5 5 5.5 6 6.5 70
50
100
150possible number of maximums in case of triplets and quadruplets
possible number of maximums in case of tripletspossible number of maximums in case of quadruplets
90 BIBLIOGRAPHY
number of sensors. Let dQij be the difference of this quality, measured by sensorsSi and Sj:dQi,j = Qj −Qi
When there are n(n-3) linear conditions on differences dQij of the form :∑dQi,i+1 = dQi,m
where 2 ≤ m ≤ N − 2 and summation is over 1 ≤ i ≤ m
Lemma:The number of sub-polygons of a n-order polygon is n(n-3). This is true for bothconvex and concave polygons, as well as planar and non-planar polygons.Proof of Lemma:Each diagonal of a polygon divides it into 2 sub-polygons. Example: diagonal ACdivides polygon ABCD into sub-polygons ABC and ACDEThe number of diagonals of a polygon is n(n-3)/2. Therefore, the number of sub-polygons of a n-order polygon is n(n-3). Lemma is prooved
Proof of TheoremLet us put all sensors Si, 1 ≤ i ≤ n as vertices of a n-order polygon. As Lemmasays, we will have n(n-3) sub-polygons (chaines of sensors Si...SjSk). For each sub-polygon (each chain) we write the following:∑
dQi,i+1
where 2 ≤ m ≤ N − 2 and summation is over 1 ≤ i ≤ mLet us rewrite dQi,j as dQi,j = Qj −Qi:TDOA-FD
14.2 The theoretical assessments of using Doppler effectcoherence equations (FD-coherence-equations) to in-crease the robustness and quality of TDOA refinment
The process of TDOA refinment in the space of TDOAsWith N microphones (1 < i, j < N), we have N(N − 1)/2 pairs p of microphones
(1 < p < N(N − 1)/2) and (N(N−1)/2)!6(N(N−1)/2−3)!
− 1 triplets p3 of pairs of microphones
(1 < p3 < (N(N−1)/2)!6(N(N−1)/2−3)!
− 1) Note, that ”-1” is added because the last triplet is
excluded because the TD-coherence-equations of this triplet is linear combination ofTD-coherence-equations of all other triplets (here we call ”TD-coherence-equations”the ordinary TDOA coherence equations from our 1st patent).
Denoting the number triplets (N(N−1)/2)!6(N(N−1)/2−3)!
− 1 as Np3 (Np3 = (N(N−1)/2)!6(N(N−1)/2−3)!
− 1),we have the same in the form 1 < p3 < Np3. The pairs p, belonging to some tripletTRIPLET (p3) can be denoted as p ∈ TRIPLET (p3).For the case of 4 microphones, we have 6 pairs of microphones (p = 1, 2, 3, 4, 5, 6)and 3 triplets of pairs of microphones (p3 = 1, 2, 3). for the case of pyramidal an-tenna, these 3 triplets correspond to 3 faces of this pyramid (the last, 4th triplet,corresponding to the 4th face of this pyramid, is excluded because the TD-coherence-equations of this triplet is linear combination of TD-coherence-equations of all othertriplets). For simplicity, we will number pairs of microphones with a single indexp. The other 2 indices of TDOAs are the moment of measurement t and numberof maximum m: TDOApm(t). For each time moment t we have the following Np3TD-coherence-equations:
∑p∈TRIPLET (p3) TDOApm(t) = 0 for 1 < p3 < Np3. The
process of refinment of TDOAs is, in fact filtering TDOApm(t) leaving only maxi-mums m who satisfy the TD-coherence-equations (
∑p∈TRIPLET (p3) TDOApm(t))
2 <
εTDcoherence with some threshold εTDcoherence: TDOApm(t).The process of TDOA refinment in the space of errors of TDOAsDenoting the errors of TDOAs as δpm(t), we can consider TD-coherence-equationsin the space, where each axis corresponds to one pair of microphones (1 < p <N(N − 1)/2). In this space we observe accuracies of TD-coherence-equations δpm(t)for different maximums m. We are interested in TDOAs who fall into hypercube ofthe size εTDOAerror in this space. We may call it εTDOAerror-hypercube.The distribution of errors in TDOAsIf we assume that the errors of TDOAs δpm(t) have the normal distribution, then thedensity of δpm(t) is high at the beginning of coordinates (point (0, 0, 0, 0, 0, 0)). Inother words, there are more small errors than big errors. The same conclusion canbe made, using the following practical reasoning: in the ideal case (no echoes, nonoise), the TDOApm(t) repersent only one ”correct” maximum m0: TDOApm0(t).In reality, there are echoes (they are usually less loud than original signal) and noise(it is also usually less loud than original signal). As a consequence, usually the”correct” maximum m0 is higher than all other maximums m, however, there couldbe times when the noise-related maximums or echo-related maximums can be higerthan the ”correct” maximum m0.The TD-coherence-equations in the space of errors of TDOAs δpm(t) represent somelinear subspace of dimension N(N − 1)/2 − Np3. For the case of 4 microphones,
92 BIBLIOGRAPHY
we have 6 pairs(N(N − 1)/2 = 4), 3 triplets(Np3 = 3), the TD-coherence-equationsrepresent a line in the space of errors of TDOAs δpm(t). We may call it ”TD-coherence-line”. Since the zero-error of TDOAs δpm(t) = 0 satisfy the TD-coherence-equations, this ”TD-coherence-line” go through the beginning of coordinates of thisspace (throught the point (0, 0, 0, 0, 0, 0)).The process of refinment of TDOAs is, in fact, leaving only maximums m correspond-ing to errors of TDOAs δpm(t) in the εTDcoherence-vicinity of this ”TD-coherence-line”.Note, that some of these δpm(t) fall into εTDOAerror-hypercube (TDOAs with smallerrors), whereas some δpm(t) fall outside εTDOAerror-hypercube (TDOAs with bigerrors).The quality of TDOA refinmentViewing the process of TDOA refinment in the space of errors of TDOAs, we mayformulate the Criteria of quality of TDOA refinment: The more δpm(t) fallinto the intersection of εTDOAerror-hypercube and εTDcoherence-vicinity of the ”TD-coherence-line”, the better TDOA refinment is provided by TD-coherence-equations.The robustness of TDOA trackingSimilarily, viewing the process of TDOA tracking in the space of errors of TDOAs,we may formulate the robustness of TDOA tracking as the quality of δpm(t)-trajectory to not go away from the beginning of coordinates with time. In otherwords, the TDOA-tracking is robust, if when TDOA-trajectory fall into εTDOAerror-hypercube at some time t, this TDOA-trajectory will remain in the εTDOAerror-hypercube.Doppler EffectThe Doppler effect is described and an equation for the frequency observed as afunction of velocity and emitted frequency is derived in the textbook. The generalDoppler equation is
f ′ = fv ± vdv ∓ vs
where f ′ is the observed frequency, f is the emitted frequency, vd is the velocity ofthe detector (or observer), vs is the velocity of the source, and v is the speed of thewave (sound) in the medium (343 m/s for sound in air).
If the speeds vs, and vr, are small compared to the speed of the wave, the re-lationship between observed frequency f and emitted frequency f0 is approximately∆f = ∆v
cf0.
The role of Doppler of TDOA trackingSimilarily to TDOAs, we denote the differencies of frequencies between pais of mi-crophones as FDpm(t) (pairs of microphones are index p, the other 2 indices of FDs
are the moment of measurement t and number of maximum m).
The velocity coherence equation for TRIPLET (p3) (one from Np3 triplets),based on Doppler effect (FD-coherence-equations) look as follows:
∑p∈TRIPLET (p3) FDpm(t) =
0 for 1 < p3 < Np3 (in the previous report it was shown that FD-coherence-equations have this form). The errors of FDs can be denoted as ∆pm(t) (similarilyto errors of TDOAs, denoted earlier as δpm(t)).
The process of refinment of FDs is, in fact filtering FDpm(t) leaving only max-imums m who satisfy the FD-coherence-equations (
∑p∈TRIPLET (p3) FDpm(t))
2 <
εFDcoherence with some threshold εFDcoherence: FDpm(t).
We will not consider here the space of errors of FDs (∆pm(t) space). Since ourgoal is to show that FD-coherence-equations improve the situation in the space oferrors of TDOAs δpm(t), we are concentrating on the connection between TDOAs andFDs. It is easy to show (see the report) that this connection has the following form:TDOApm(t+1)−TDOApm(t) = FDpm(t) · TimeStep
SoundFrequency. Or, in the space of errors
of TDOAs, (δpm(t+1)− δpm(t))2 ≤ ∆pm(t) · TimeStep
SoundFrequency. This inequality provides
the increasing of robustness of TDOA tracking(see above) by using FD-coherence-equations (if TDOA-trajectory fall into εTDOAerror-hypercube at some time t, thisinequality will force TDOA-trajectory to remain inside the εTDOAerror-hypercube).Also, this inequality provides the higher quality of TDOA refinment(see above),because the normal distribution of errors in TDOAs (or, even more generally, becausethere are more small errors than big errors).
The figure 53 illustrates the abovementioned shows ”TD-coherence-line” andεTDcoherence-vicinity of this ”TD-coherence-line” (provided by TD-coherence-equations),as well as εTDOAerror-hypercube (rectangle) in time-moments t and t+1.
The inequality (δpm(t + 1) − δpm(t))2 ≤ ∆pm(t) · T imeStep
SoundFrequencyprovides the
increasing of robustness of TDOA tracking, because if TDOA-trajectory fall intoεTDOAerror-hypercube at some time t, this inequality will force TDOA-trajectory toremain inside the εTDOAerror-hypercube.
94 BIBLIOGRAPHY
Figure 53: The geometry of recording microphones, allowing TDOA refinment toeliminate echoes (left) and not allowing to eliminate echoes (right).
% CALCULATE TDOA(PATENT1) OR FD+TDOA(PATENT2) USING(OR NOT USING) COHERENCE EQUATIONS%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SET DIRECTORIES, NUMBER OF CORRELATION MAXIMUMS, TIME−WINDOW, ETC%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
warning o f f a l l ; c l o s e a l l ; c l e a r a l l ; g lobDirIN=’/NAS3/ALES/DATA/ ’ ; %globDirOUT=’C: \ ’ ;globDirOUT=’/NAS3/ALES/ ’ ; globDirIN=’ml8b\ERMITES\DATA\ ’ ; globEXTpar . INsubDODO=’CENT16NOV/ ’ ;
globDirOUT=’/NAS3/ALES/CHIROcopyNAS3GLOTIN/RESULTS/PATENT1nocoher / ’ ;n o n d i f f i c i l e s =[56 1 6 7 2 5 46 47 48 49 50 51 52 53 40 41 42 43 44 29
30 31 3 2 ] ; p e u d i f f i c i l e s =[55 21 9 10 33 36 37 45 3 8 ] ;t r e s d i f f i c i l e s =[23 26 28 12 13 14 15 16 34 35 39 ] ; t r o p d i f f i c i l e s
2000 ] , [ 2000 2250 ] , [ 2250 2500 ] , [ 2500 2750 ] , [ 2750 3000 ] , [ 3000 3250 ] , [ 3250 3500 ]} ;AllSegmentTrajectoryMS=c e l l (1 , Nsegs ) ; f o r i s e g =1:Nsegs , AllSegmentTrajectoryMS{ i s e g }=[500+( i s eg −1)
∗Lseg 500+ i s e g ∗Lseg ] ; end ;RECStoDOjustdoCUBE=[33 49 38 9 21 23 12 1 26 56 2 8 ] ;RECStoDOdoAllForfdfd2=s e t d i f f ( 1 : 5 4 ,RECStoDOjustdoCUBE) ;RECStoDOdoAllFortdoaIfdm1=s e t d i f f ( 1 : 5 4 , [ RECStoDOjustdoCUBE [15 16 17 6 7 2 8 3 4 10 5 11 13
1 4 ] ] ) ;% ! ! ! ! ! ! ! ! ! ! ! ! ! ! SWITCH 0+ => a l l f r e qu en c i e s 104+ => LOW f r equ en c i e s 2∗104+ => HIG
f r e qu en c i e s ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !RECStoDOnew=[0+[56 1 26 28 9 ] 104+[56 1 26 28 9 ] 2∗104+[56 1 26 28 9 ] 104+[21 23 12 33 49
38 ] 2∗104+[21 23 12 33 49 38 ] ] ;RECStoDOpat2=[12 33 38 4 9 ] ;RECplos=[ s e t d i f f (RECStoDOdoAllFortdoaIfdm1 ,RECStoDOpat2) s e t d i f f ( 1 : 5 7 , [ RECStoDOpat2
RECStoDOdoAllFortdoaIfdm1 ] ) ] ;%s e t d i f f ( 1 : 5 4 , [ 5 6 1 26 28 9 21 23 12 33 49 38 ] ) ;%justdoCUBE=ONLY AFTER calcTDOA , l i n e=load ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor
) globEXTcourt{ i chant } ’TDOA’ num2str ( [ C percTDOArefine ] ) ’ percSegm ’ num2str (iSegmentTrajectory ) ’ . mat ’ ] ) ;
globEXTrangeKHZcell{ i chant }(1)+globEXTrangeKHZcell{ i chant }(2) ) / 2 ] ;globEXTrangeKHZcell{ i chant+2∗nb fichierNOFRBAND}=[( globEXTrangeKHZcell{ i chant }(1)+
globEXTrangeKHZcell{ i chant }(2) ) /2 globEXTrangeKHZcell{ i chant }(2) ] ;end ;globEXTrangeKHZmat=ce l l2mat ( globEXTrangeKHZcell ) ;datafrom0cyberio1dodo=1;f o r i chant =1: n b f i c h i e ri f ichant >2∗nb fichierNOFRBANDglobEXTpath{ i chant }=[globEXTpath0{ i chant } ’ . wav ’ ] ; globEXTcourt{ i chant }=[globEXTpath0{ i chant
} ( 1 : 5 ) ’HIG’ ] ;datafrom0cyberio1dodo=0;e l s e i f ichant>nb fichierNOFRBANDglobEXTpath{ i chant }=[globEXTpath0{ i chant } ’ . wav ’ ] ; globEXTcourt{ i chant }=[globEXTpath0{ i chant
} ( 1 : 5 ) ’LOW’ ] ;datafrom0cyberio1dodo=0;e l s e i f ichant<=57
15. APPENDIX4: MATLAB CODE FOR SIMULATION 99
globEXTpath{ i chant }=[globEXTpath0{ i chant } ’ . wav ’ ] ; globEXTcourt{ i chant}=globEXTpath0{ i chant} ( 1 : 8 ) ;
datafrom0cyberio1dodo=0;e l s eglobEXTpath{ i chant}=globEXTpath0{ i chant } ; globEXTcourt{ i chant}=globEXTpath0{ i chant } ( [ 1 5 : 2 0 39
mkdir ( globDirOUT , globEXTpar .OUTsub) ;mid=505; edge=101; i f edge <1000 , s t r edge =[ ’0 ’ num2str ( edge ) ] ; e l s e s t r edge=num2str ( edge ) ; end ;
i f mid<1000 , strmid =[ ’0 ’ num2str (mid ) ] ; e l s e strmid=num2str (mid ) ; end ;
ALL5hz1w1maxs2pers2=c e l l (1 , l ength ( AllSegmentTrajectoryMS ) ) ; f o r i j i =1: l ength (ALL5hz1w1maxs2pers2) ,ALL5hz1w1maxs2pers2{ i j i }=wrev (wrev (vars2Dmp+10000∗vars2Dhzw ) ) ; end ;
% 41−>216 ALLbatNums=[49 ] ; ALLbatNums=[49:56 47 43 :45 36 :41 31 :34 23 :29 21 : −1 :1 ] ;msize=4; f s i z e =10; f s izeSm=6; f s i z eB i g =18; s t r type={ ’Orig ’ , ’ VerLn ’ , ’ AbsVerLn ’ } ; cortype={ ’
hist0zoom1artzoom2no3=3; l abe l 0 range1 s imp l e =1; vecPlotZoomsOfCorrs = [ ] ; %[1 7 ] ;p l o tSpec t r =0; p lo tPar t sSpec t r =0; p l o tF i l t e r edPa r t sSp e c t r =0;plotCORRS=0; plotCORiTDOA=0;
%=====================================================================% FREEQUENCIES, ANTENNAS GEOMETRIES%=====================================================================f minHz=1000∗min(min (globEXTrangeKHZmat ) )∗ones (1 , n b f i c h i e r ) ; f maxHz=1000∗max(max(
globEXTrangeKHZmat ) )∗ones (1 , n b f i c h i e r ) ;% f minHz ( [ 1 : 7 ] ) =1000∗[40 20 70 95 20 33 40]−10;% f maxHz ( [ 1 : 7 ] ) =1000∗[83 50 90 115 55 53 60]+10;f minHz=1000∗globEXTrangeKHZmat ( : , 1 ) ’ ;f maxHz=1000∗globEXTrangeKHZmat ( : , 2 ) ’ ;indf min=25; indf max=80;
ones (1 , nb f i c h i e r −28) ] ;Nantenne=6;antenneBourgesPosMic=c e l l (1 , Nantenne ) ;antenneBourgesPosMic {1}=[0 0 −0.6 0 .6 ; 0 0 .692 −0.346 −0.346; 0 .99 0 0 0 ] ’ ;antenneBourgesPosMic {2}=[0 ,39 ,0 ; 0 , 0 , 32 .4 ; −34.6 ,−21.2 ,0 ; 33 .2 , −18 .3 ,0 ]/100 ;antenneBourgesPosMic {3}=[0 ,39 ,0 ; 0 , 0 , 32 .4 ; −33 ,−18.5 ,0 ; 34 .5 , −16 ,0 ]/100 ;antenneBourgesPosMic {4}=[12.5 , −7.5 ,27.8 ; 0 ,0 ,5 ; 0 , 14 . 5 , 2 7 . 5 ; −13 ,−7.5 ,27]/100;antenneBourgesPosMic {5}=[−0.335 0 0 .335 0 ; −0.215 0 −0.215 0 . 3 75 ; 0 0 .324 0 0 ] ’ ;antenneBourgesPosMic {6}=[0 0 0 .6 −0.6 ; 0 0 .7 −0.346 −0.346; 1 0 0 0 ] ’ ;f r eq0 =50000; f c o e f =7; Nmic=4; Nraz=4; waterbyair =1500/331; hydrobymicro=7000/1; ArrTimeErrRatio
100 BIBLIOGRAPHY
=hydrobymicro/waterbyair ;%−−−−−−−−−−−−−−−−−−−ORIGf e =48000; % frequence d ’ ? ?? chan t i l l onag efen=10∗ f e ; % f e n e t r e s de 10 secondesnb sum=100; % d i v i s i o n de l ’ e chan t i l l onag e par 100c s t =6; nb s igb =100; e p r a i e =0.02; s i g s e g =50; s i g p a s =10; th r e s =3; nb plan=3; rou=1; s e v e r a l =1; c=1500;rep=pwd ;nom fich =[ ’3M ch4 ’ ; ’ 3 M ch5 ’ ; ’ 3 M ch6 ’ ; ’ 3 S ch4 ’ ; ’ 3 S ch5 ’ ; ’ 3 S ch6 ’ ; ’ 1 0 M ch2 ’ ; ’ 1 0 M ch3
’ ; ’ 1 0 S ch1 ’ ; ’ 1 0 S ch5 ’ ; ’ 1 0 S ch7 ’ ] ;% i f s e v e r a l==1% nb f i c h i e r =4;micro=[1 2 3 6 ] ; %nb td=15;% e l s e% nb f i c h i e r =5;micro=[7 8 9 1 0 ] ; %nb td=5;% end%−−−−−−−−−−−−−−−−−−−ORIG−TO−MYfe =250000; % frequence d ’ ? ?? chan t i l l onag e = 250 000 pts par seconde
indINmsec=f e /1000;f en =0.1∗ f e ; % f e n e t r e s de 0 .1 secondesnb sum=1;c s t =0.99599∗( f e /nb sum) ;micro=[1 2 3 4 ] ;e p r a i e=ep r a i e /ArrTimeErrRatio ;indVms=( fen /1000) ;
antenneBourgesDistMicros=c e l l (1 , Nantenne ) ;f o r iantenne=1:NantenneantenneBourgesDistMicros{ iantenne}=zero s (Nmic , Nmic) ;end ;antenneBourgesDistMicros {1}(1 ,3) =67;antenneBourgesDistMicros {1}(1 ,2)= 55 . 5 ;antenneBourgesDistMicros {1}(3 ,4) =68;antenneBourgesDistMicros {1}(3 ,2)= 51 ;antenneBourgesDistMicros {1}(1 ,4) =66.5;antenneBourgesDistMicros {1}(3 ,4)= 49 ;antenneBourgesDistMicros {2}(1 ,3) =46.5;antenneBourgesDistMicros {2}(1 ,2) =51;antenneBourgesDistMicros {2}(3 ,4) =47;antenneBourgesDistMicros {2}(3 ,2) =50;antenneBourgesDistMicros {2}(1 ,4) =46.5;antenneBourgesDistMicros {2}(3 ,4) =49;
%=====================================================================% CYCLE OVER ALL CORRELATION POSSIBILITIES : ’ unbiased ’ , ’ biased ’ , ’ none ’%=====================================================================fo r C bias=ALLbiasi f C bias==1BegBias=’u ’ ; StrCors=’unbiased ’ ;e l s e i f C bias==2BegBias=’b ’ ; StrCors=’biased ’ ;e l s e i f C bias==3BegBias=’n ’ ; StrCors=’none ’ ;end ;
plotTIMEall=c e l l ( nb f i c h i e r , l ength ( micro ) , l ength ( micro ) ) ;plotTDOAall=c e l l ( nb f i c h i e r , l ength ( micro ) , l ength ( micro ) ) ;
% f o r i f i l e=ichant% f o r i=micro% f o r j=micro% i f i>=j , cont inue ; end ;% plotTIMEall{ i f i l e , i , j}=− i n f ∗ones (1 , l ength ( vecTimesTracking ) ) ;% end ;% end ;% end ;N t r i p l e t s=ze ro s (max(ALLbatNums) , l ength ( AllSegmentTrajectoryMS ) , l ength (ALL5hz1w1maxs2pers2{1}) ) ;tota lErrorMin=i n f ∗ones (max(ALLbatNums) , l ength ( AllSegmentTrajectoryMS ) , l ength (ALL5hz1w1maxs2pers2
{1}) ) ;totalErrorMax=−i n f ∗ones (max(ALLbatNums) , l ength ( AllSegmentTrajectoryMS ) , l ength (
ALL5hz1w1maxs2pers2{1}) ) ;t o t a lE r r o r=ze ro s (max(ALLbatNums) , l ength ( AllSegmentTrajectoryMS ) , l ength (ALL5hz1w1maxs2pers2{1}) ) ;totalTime=ze ro s (max(ALLbatNums) , l ength ( AllSegmentTrajectoryMS ) , l ength (ALL5hz1w1maxs2pers2{1}) ) ;tota lErrorN=ze ro s (max(ALLbatNums) , l ength ( AllSegmentTrajectoryMS ) , l ength (ALL5hz1w1maxs2pers2{1}) )
;
15. APPENDIX4: MATLAB CODE FOR SIMULATION 101
cube=ze ro s ( l ength ( AllSegmentTrajectoryMS ) , l ength (ALLmaxs) ,17) ; squareRes=ze ro s ( l ength (AllSegmentTrajectoryMS ) ,17) ;
%=====================================================================% CYCLE OVER ALL SEGMENTS OF TRAJECTORY (TIME−STEPS)%=====================================================================fo r iSegmentTrajectory=1: l ength ( AllSegmentTrajectoryMS )N2Darr DivsMaxsPers=34;Ntdoa2D=c e l l (1 , N2Darr DivsMaxsPers ) ; ALLmaxsBYperc=c e l l ( N2Darr DivsMaxsPers , l ength (
AllSegmentTrajectoryMS ) ) ;f o r i o i =1:N2Darr DivsMaxsPersNtdoa2D{ i o i}=zero s (max(ALLbatNums) , l ength (ALL5hz1w1maxs2pers2{ iSegmentTrajectory }) ) ;ALLmaxsBYperc{ i o i , iSegmentTrajectory}=zero s ( l ength (ALLmaxs) , l ength (ALLperc ) ) ;end ;
%=====================================================================% CYCLE OVER ALL DESIRED PERCENTAGES OF LEFT−TDOA%=====================================================================fo r C DivsMaxsPers=ALL5hz1w1maxs2pers2{ iSegmentTrajectory} %ODNO v FORindC DivsMaxsPers=f ind ( C DivsMaxsPers==ALL5hz1w1maxs2pers2{ iSegmentTrajectory } , 1 , ’ f i r s t ’ ) ;maxClickFreqHz=f i x ( C DivsMaxsPers /100000) ;windowSize=f i x ( ( C DivsMaxsPers−maxClickFreqHz ∗100000) /10000) ;nb td=f i x ( ( C DivsMaxsPers−maxClickFreqHz∗100000−windowSize ∗10000) /10) ;C pers=f i x ( C DivsMaxsPers−maxClickFreqHz∗100000−windowSize∗10000−nb td ∗10) ;%nb td=nb td ∗2 ;windowSize=windowSize /2 ; maxClickFreqHz=maxClickFreqHz ∗5 ; Nmax=nb td ; vecALL percTDOArefine=so r t
( [ OBLIGATvecALL percTDOArefine C pers ] ) ;
f o r C cortype=[3∗2+1] %[3∗2+1 3 6 2 5 3∗2+3] %wrev ([3∗2+1 3 6 2 5 3∗2+3]) %1:3∗2+1BegFi le=[ cortype{C cortype} num2str (maxClickFreqHz ) ’ hz ’ num2str ( windowSize ) ’w’ num2str ( nb td )
’m’ num2str ( C pers ) ’ p ’ BegBias ] ;
%−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−% MODEL ( IF WE WANT ADDITIONALLY TO MAKE ARTIFICIAL SIMULATIONS WITH THESE PARAMETERS)%−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−f o r C model =[ ]Ntraj =90; maxUturn=15; lenUturn=round ( Ntraj /maxUturn ) ;cent sens=mean( sensS1XY2 , 1 ) ;%−−−−−−ALLOCATION−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−arrsecsS1TR2=ze ro s (Nmic , Ntraj ) ;%projvelS1TR2=ze ro s (Nmic , Ntraj ) ;%arrfreqS1TR2=ze ro s (Nmic , Ntraj ) ;
veltosensTR1S2=ze ro s ( Ntraj , Nmic , 3 ) ;vectosensTR1S2XY3=ze ro s ( Ntraj , Nmic , 3 ) ;absdisttosensTR1S2=ze ro s ( Ntraj , Nmic) ;
batvelsTR1XY2=ze ro s ( Ntraj , 3 ) ;batmtrsTR1XY2=ze ro s ( Ntraj , 3 ) ;
TDOAsecsTR1S23=ze ro s ( Ntraj , Ntraj , Ntraj ) ;FDODhzTR1S23=ze ro s ( Ntraj , Ntraj ) ;%−−−−−−TRAJECTORY, CHANT −−−−−−−−−−−−−−−−−−−−−−−−−−−−−−bat s ec s =1:Ntraj ; maxRandVel=round (maxVel /4) ;% batvelsTR1XY2 ( : , 1 )=maxVel∗ s i n ( l i n s pa c e ( sensS1XY2 (1 ,1 ) , sensS1XY2 (2 ,1 ) , Ntraj ) ∗2∗ pi ∗maxUturn/
Ntraj )−maxRandVel+randi (maxRandVel , [ 1 , Ntraj ] ) ;% batvelsTR1XY2 ( : , 2 )=maxVel∗ cos ( l i n s pa c e ( sensS1XY2 (1 ,2 ) , sensS1XY2 (2 ,2 ) , Ntraj ) ∗2∗ pi ∗maxUturn/
Ntraj )−maxRandVel+randi (maxRandVel , [ 1 , Ntraj ] ) ;% batvelsTR1XY2 ( : , 3 )=maxVel∗ s i n ( l i n s pa c e ( sensS1XY2 (3 ,3 ) , sensS1XY2 (4 ,3 ) , Ntraj ) ∗2∗ pi ∗maxUturn/
DomTreug2=[ l i n s pa c e (0 ,maxVel /3 , lenUturn ) l i n s pa c e (maxVel /3 ,0 , lenUturn ) ] ;DomTrap3=[ l i n s pa c e (0 ,maxVel /3 , lenUturn ) l i n s pa c e (maxVel /3 ,maxVel /3 , lenUturn ) l i n s pa c e (maxVel
/3 ,0 , lenUturn ) ] ;GalkaLin3=[ l i n s pa c e (maxVel /3 ,0 , lenUturn ) l i n s pa c e (0 ,maxVel /3 , lenUturn ) l i n s pa c e (maxVel /3 ,maxVel
/3 , lenUturn ) ] ;LinGalka3=[ l i n s pa c e (maxVel /3 ,maxVel /3 , lenUturn ) l i n s pa c e (maxVel /3 ,0 , lenUturn ) l i n s pa c e (0 ,maxVel
/3 , lenUturn ) ] ;Galka2=[ l i n s pa c e (maxVel /3 ,0 , lenUturn ) l i n s pa c e (0 ,maxVel /3 , lenUturn ) ] ;
batvelsTR1XY2 ( : , 1 ) =[DomTreug2 GalkaLin3 l i n s pa c e (0 ,−maxVel /2 , lenUturn ) DomTreug2 l i n s pa c e (0 ,maxVel /2 ,2∗ lenUturn ) −GalkaLin3 l i n s pa c e (maxVel /2 ,0 ,2∗ lenUturn ) ]−maxRandVel+randi (maxRandVel , [ 1 , Ntraj ] ) ;
102 BIBLIOGRAPHY
batvelsTR1XY2 ( : , 2 ) =[LinGalka3 Galka2 −LinGalka3 l i n s pa c e (0 ,maxVel ,2∗ lenUturn ) Galka2 l i n s pa c e (−maxVel , maxVel ,1∗ lenUturn ) l i n s pa c e (maxVel,−maxVel ,2∗ lenUturn ) ]−maxRandVel+randi (maxRandVel, [ 1 , Ntraj ] ) ;
batmtrsTR1XY2 ( 1 , : )=cent sens ;f o r i t r a j =2: NtrajbatmtrsTR1XY2( i t r a j , : )=batmtrsTR1XY2( i t r a j −1 , :)+(bat s e c s ( i t r a j )−bat s ec s ( i t r a j −1) ) .∗ (
batvelsTR1XY2 ( i t r a j , : )+batvelsTR1XY2 ( i t r a j −1 , :) ) /2 ;end ;
%−−−−Secs ( d i s t t o s e n s ) and Hz( v e l t o s en s ) rece ived−−−−−−f o r i s e n s =1:Nmicf o r i t r a j =1: Ntraj
vectosensTR1S2XY3 ( i t r a j , i s ens , : )=sensS1XY2 ( i s ens , : )−batmtrsTR1XY2( i t r a j , : ) ;absdisttosensTR1S2 ( i t r a j , i s e n s )=sq r t ( squeeze ( vectosensTR1S2XY3 ( i t r a j , i s ens , : ) ) ’∗ squeeze (
vectosensTR1S2XY3 ( i t r a j , i s ens , : ) ) ) ;arrsecsS1TR2 ( i s ens , i t r a j )=absdisttosensTR1S2 ( i t r a j , i s e n s ) /c ;
veltosensTR1S2 ( i t r a j , i s e n s )=−( f r eq0 /c ) ∗( batvelsTR1XY2 ( i t r a j , : ) ∗ squeeze ( vectosensTR1S2XY3 (i t r a j , i s ens , : ) ) ) / absdisttosensTR1S2 ( i t r a j , i s e n s ) ;
end ;end ;
%−−−−−TDOAsecsTR1S23 and FDODhzTR1S23−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−f o r i s e n s =1:Nmicf o r j s e n s =1:Nmic
TDOAsecsTR1S23 ( : , i s ens , j s e n s )=(arrsecsS1TR2 ( i s ens , : )−arrsecsS1TR2 ( j sens , : ) ) ’ ;FDODhzTR1S23 ( : , i s ens , j s e n s )=(veltosensTR1S2 ( : , i s e n s )−veltosensTR1S2 ( : , j s e n s ) ) ;
end ;end ;otsup=7;f o r i t r a j =2: Ntrajf i g u r e ;p lo t3 (batmtrsTR1XY2 ( 1 : i t r a j , 1 ) , batmtrsTR1XY2 ( 1 : i t r a j , 2 ) , batmtrsTR1XY2 ( 1 : i t r a j , 3 ) ) ;ax i s ( [ 0 maxXYZ 0 maxXYZ 0 maxXYZ] ) ;ax i s o f f ;view ( [ 0 0 1 ] ) ;TDOAcurr=ze ro s (Nmic , Nmic) ; FDODcurr=ze ro s (Nmic , Nmic) ;TDOAsum=0; FDODsum=0;f o r i s e n s =1:Nmic
text ( sensS1XY2 ( i s ens , 1 )−otsup , sensS1XY2 ( i s ens , 2 )−otsup , sensS1XY2 ( i s ens , 3 )−otsup , [ ’ SENS’num2str ( i s e n s ) ] ) ;
f o r j s e n s =1:Nmici f j s ens−i s e n s ˜=1 && jsens−i s e n s ˜=−3, cont inue ; end ; %TODELl i n e ( [ c ent sens (1)−otsup cent sens (1)+otsup ] , [ c ent sens (2 ) cent sens (2 ) ] , [ c ent sens (3 )
cent sens (3 ) ] ) ;l i n e ( [ c ent sens (1) cent sens (1) ] , [ c ent sens (2 )−otsup cent sens (2 )+otsup ] , [ c ent sens (3)
cent sens (3 ) ] ) ;TDOAcurr( i s ens , j s e n s )=round (TDOAsecsTR1S23( i t r a j , i s ens , j s e n s ) ∗1000) ;FDODcurr( i s ens , j s e n s )=round (FDODhzTR1S23( i t r a j , i s ens , j s e n s ) ) ;t ext ( ( sensS1XY2 ( i s ens , 1 )+sensS1XY2 ( j sens , 1 ) ) /2 −otsup . . .
, ( sensS1XY2 ( i s ens , 2 )+sensS1XY2 ( j sens , 2 ) ) /2 −otsup . . ., ( sensS1XY2 ( i s ens , 3 )+sensS1XY2 ( j sens , 3 ) ) /2 −otsup . . ., [ num2str (TDOAcurr( i s ens , j s e n s ) ) ’ms ’ num2str (FDODcurr( i s ens , j s e n s ) ) ’Hz ’ ] ) ;
TDOAsum=TDOAsum+TDOAcurr( i s ens , j s e n s ) ; FDODsum=FDODsum+FDODcurr( i s ens , j s e n s ) ;end ;
end ;text (4∗ otsup , ( sensS1XY2 (4 ,2 )+sensS1XY2 (1 ,2 ) ) /2 −4∗otsup , 0 , [ ’ Sum of TDOA( ’ num2str ( i t r a j ) ’ ) =’
num2str (TDOAsum) ’ms ’ ] ) ;t ext (4∗ otsup , ( sensS1XY2 (4 ,2 )+sensS1XY2 (1 ,2 ) ) /2 −5∗otsup , 0 , [ ’ Sum of FDOD’ num2str ( i t r a j ) ’ ) =’
num2str (FDODsum) ’Hz ’ ] ) ;t i t l e ( ’ TimeDif ference o f S i gna lAr r i va l , ms and FrequencyDi f f e rence o f Arr ivedSigna l , Hz ’ ) ;F( i t r a j −1) = getframe ( gc f ) ;%R = input ( [ ’ p r e s s ente r f o r time moment N’ num2str ( i t r a j ) ] )saveas ( gcf , [ globDirOUT globEXTpar .OUTsub ’28 m tra jPos i t i on ’ num2str ( i t r a j ) ’TRAJECT. jpg ’ ] ) ;c l o s e a l l ;end ;movie (F , 1 ) ;movie2avi (F , [ globDirOUT globEXTpar .OUTsub ’28m trajMovie ’ ] , ’ compression ’ , ’ None ’ )end ;Col={ ’g ’ , ’ c ’ , ’m’ , ’ r ’ , ’ b ’ , ’ y ’ } ;S ty l e ={ ’ − . ’ , ’ : ’ , ’ : ’ , ’ − . ’ , ’ − . ’ , ’ : ’} ;
15. APPENDIX4: MATLAB CODE FOR SIMULATION 103
Mark={ ’∗ ’ , ’< ’ , ’ s ’ , ’ o ’ , ’ d ’ , ’+ ’} ;f o r C plotAntenne =[ ]hold on ;f o r iantenne=1:Nantennef o r i c =1:8
o=randperm (4) ;p lo t3 ( squeeze ( permute ( antenneBourgesPosMic{ iantenne }(o , 1 ) , o ) ) . . .
, squeeze ( permute ( antenneBourgesPosMic{ iantenne }(o , 2 ) , o ) ) . . ., squeeze ( permute ( antenneBourgesPosMic{ iantenne }(o , 3 ) , o ) ) . . ., ’ Color ’ , Col{ i antenne } , ’ L ineSty le ’ , S ty l e { iantenne } , ’ MarkerSize ’ , 1 0 , ’Marker ’ ,Mark{
iantenne } ) ;endend ;g r id on ;view (45 ,45) ;x l ab e l ( [ ’ anthena width ,m’ ] , ’ FontSize ’ , f s i z e ) ;y l ab e l ( [ ’ anthena length ,m’ ] , ’ FontSize ’ , f s i z e ) ;z l a b e l ( [ ’ anthena height ,m’ ] , ’ FontSize ’ , f s i z e ) ;mins=min ( ce l l 2mat ( antenneBourgesPosMic ’ ) , [ ] , 1 ) ;maxs=max( ce l l2mat ( antenneBourgesPosMic ’ ) , [ ] , 1 ) ;xlim ( [ mins (1) maxs (1) ] ) ;ylim ( [ mins (2) maxs (2) ] ) ;z l im ( [ mins (3 ) maxs (3) ] ) ;l egend ( antenneStr ) ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub ’ pos mic . jpg ’ ] ) ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub ’ pos mic . f i g ’ ] ) ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub ’ pos mic . eps ’ ] , ’ psc2 ’ ) ;c l o s e a l l ;end ;maxAmpl=111;%−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−% CYCLE OVER ALL DATA (NOW ONLY ONE RECORDING ”oneforALLbatNums==ALLbatNums” IS USED)%−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−f o r i chant=ALLbatNums %nb f i c h i e r :−1:1 %[29 :32 ] %[2:5 1 6 7 ] %1:7 %wrev ( [ 1 5 2 4 3 7 6 ] )[ ’ f o r i chant=’ globEXTcourt{ i chant } ’ C DivsMaxsPers=’ num2str ( C DivsMaxsPers ) ’
iSegmentTrajectory=’ num2str ( iSegmentTrajectory ) ]i f i chant==56, f e =312000; end ;
iSegmentTrajectory }(1) ) /ovMYmsec ;otstup=4; otstup=0; %do 7okt bylo otstup=4;s i zeTrack=(AllSegmentTrajectoryMS{ iSegmentTrajectory }(2)−AllSegmentTrajectoryMS{
iSegmentTrajectory }(1)−otstup∗ovMYmsec) /ovMYmsec ; % s izeTrack=(4000−6∗ovMYmsec) /ovMYmsec ;
i f i chant==57, startMYmsec=7000; s i zeTrack=(AllSegmentTrajectoryMS{ iSegmentTrajectory }(2)−4∗ovMYmsec) /ovMYmsec ; end ;
nb secMYmsec=startMYmsec+s izeTrack ∗ovMYmsec ;vecTimesTracking=startMYmsec :1∗ovMYmsec : nb secMYmsec ; %vecTimesTracking=startMYmsec :1∗ovMYmsec :
nb secMYmsec−ovMYmsec ;t minMs= vecTimesTracking−windowSize∗ovMYmsec ; t maxMs=vecTimesTracking+windowSize∗ovMYmsec ;INDvecTimesTracking=vecTimesTracking∗ indINmsec ;%i f windowSize==1ALLdescrStrCour=[ ’ dt=’ num2str (ovMYmsec) ’ms ’ ] ;%e l s eALLdescrStr =[ ’ dt=’ num2str (ovMYmsec) ’ms , timeWindow=’ num2str ( windowSize∗ovMYmsec) ’ms ’ ] ;%end ;pos mic=antenneBourgesPosMic{anteneINDbat ( i chant ) } ;maxTDOAm=abs (max(max( pos mic ) )−min(min ( pos mic ) ) ) ;maxTDOAms=1000∗maxTDOAm/c ;f o r C 2DcorLIKEHOOD=[1]%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% CORRELATION OF SPECTROGRAMS,TDOAiFD (C 2Dspect2DcorTDiFD ) , smooth AND plo t %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%fo r C 2DcorTDOA=[1]
f o r C 2Dspect2DcorTDiFD=[1]i f datafrom0cyberio1dodo==0 %ichant<=57
[ globDirIN globEXTpath{ i chant } ]v=wavread ( [ globDirIN globEXTpath{ i chant } ] ) ;
e l s eload ( [ globDirIN globEXTpar . INsubDODO globEXTpath{ i chant } ] ) ;v=tcu r de t ; c l e a r t cu r de t ;
end ;f o r imic=1:Nmic
s i g=v ( : , imic ) ;
104 BIBLIOGRAPHY
[ ysAllTime , fsAllTime , tsAllTime , psAllTime ]= spectrogram ( s ig ,256 ,0 ,256 ,250000 , ’ yaxis ’ ) ;d i s t=abs ( fsAllTime−f minHz ( i chant ) ) ;indf min=f ind ( d i s t==min( d i s t ) , 1 , ’ f i r s t ’ ) ;d i s t=abs ( fsAllTime−f maxHz ( ichant ) ) ;indf max=f ind ( d i s t==min( d i s t ) , 1 , ’ l a s t ’ ) ;
f o r i t =1: l ength ( vecTimesTracking )d i s t=abs (1000∗ tsAllTime−t minMs ( i t ) ) ;indt min ( i t )=f i nd ( d i s t==min( d i s t ) , 1 , ’ f i r s t ’ ) ;d i s t=abs (1000∗ tsAllTime−t maxMs( i t ) ) ;indt max ( i t )=f i nd ( d i s t==min( d i s t ) , 1 , ’ l a s t ’ ) ;
end ;indt mid=round ( ( indt min+indt max ) /2) ;
i f p l o tSpec t r==1f i g u r e ;imagesc ( tsAllTime , fsAllTime , log10 ( abs ( psAllTime ) ) ) ;ax i s xy ; t i t l e ( ’ spectrogramme ’ , ’ FontSize ’ , f s i z eB i g ) ;y l ab e l ( ’ Frequences Hz ’ , ’ FontSize ’ , f s i z eB i g ) ;x l ab e l ( ’Temps en s ’ , ’ FontSize ’ , f s i z eB i g )colormap ( f l i p ud ( gray ) )saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{ i chant } ’ iSegm ’
saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{ i chant } ’ iSegm ’num2str ( iSegmentTrajectory ) ’ allSPECT mic ’ num2str ( [ imic ] ) ’ . f i g ’ ] ) ;
end ;
PercentShow (0 , 1 , ’ c a l c IM . . . ’ , [ ] , f a l s e ) ;f o r i t =1: l ength ( vecTimesTracking )
PercentShow ( i t , l ength ( vecTimesTracking ) , [ da t e s t r ( c l o ck ) ’ c a l c IM f o r ’globEXTcourt{ i chant } ] , [ ] , t rue ) ;
ps=psAllTime ( indf min : indf max , indt min ( i t ) : indt max ( i t ) ) ;ys=ysAllTime ( indf min : indf max , indt min ( i t ) : indt max ( i t ) ) ;f s=fsAl lTime ( indf min : indf max ) ;t s=tsAllTime ( indt min ( i t ) : indt max ( i t ) ) ;i f p l o tPar t sSpec t r==1
f i g u r e ;imagesc ( ts , f s , log10 ( abs ( ps ) ) ) ;ax i s xy ; t i t l e ( ’ spectrogramme ’ , ’ FontSize ’ , f s i z eB i g ) ;y l ab e l ( ’ Frequences Hz ’ , ’ FontSize ’ , f s i z eB i g ) ;x l ab e l ( ’Temps en s ’ , ’ FontSize ’ , f s i z eB i g )colormap ( f l i p ud ( gray ) )saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{ i chant } ’
iSegm ’ num2str ( iSegmentTrajectory ) ’ partSPECT mic ’ num2str ( [ imic ] ) ’time=’ num2str ( [ indt min ( i t ) indt max ( i t ) ] ) ’ . jpg ’ ] ) ;
time=’ num2str ( [ indt min ( i t ) indt max ( i t ) ] ) ’ . eps ’ ] , ’ psc2 ’ ) ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{ i chant } ’
iSegm ’ num2str ( iSegmentTrajectory ) ’ partSPECT mic ’ num2str ( [ imic ] ) ’time=’ num2str ( [ indt min ( i t ) indt max ( i t ) ] ) ’ . f i g ’ ] ) ;
end ;
ntb=999; nfb=f c o e f ∗( indf max−indf min ) ;tb=l i n spa c e (min ( t s ) ,max( t s ) , ntb ) ;fb=l i n spa c e (min ( f s ) ,max( f s ) , nfb ) ;
[ tsg , f s g ] = meshgrid ( ts , f s ) ;[ tbg , fbg ] = meshgrid ( tb , fb ) ;pb = inte rp2 ( tsg , f sg , ps , tbg , fbg ) ;
IM{2 , imic , i t}=im f i l t e r (IM{1 , imic , i t } ,V) ;IM{3 , imic , i t}=abs (IM{2 , imic , i t}−mean(mean(IM{2 , imic , i t }) ) ) ;i f p l o tF i l t e r edPa r t sSpe c t r==1
f o r type=1:3f i gu r e ,imagesc ( tb , fb , IM{type , imic , i t }) ;
15. APPENDIX4: MATLAB CODE FOR SIMULATION 105
ax i s xy ; t i t l e ( s t r type { type } , ’ FontSize ’ , f s i z eB i g ) ;y l ab e l ( ’ Frequences Hz ’ , ’ FontSize ’ , f s i z eB i g ) ; x l ab e l ( ’Temps en s ’ , ’
FontSize ’ , f s i z eB i g )colormap ( f l i p ud ( gray ) )saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{ i chant }
’ iSegm ’ num2str ( iSegmentTrajectory ) ’ f i ltSPECT mic ’ s t r type { type}’ mic=’ num2str ( [ imic ] ) ’ . jpg ’ ] ) ;
saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{ i chant }’ iSegm ’ num2str ( iSegmentTrajectory ) ’ f i ltSPECT mic ’ s t r type { type}’ mic=’ num2str ( [ imic ] ) ’ . eps ’ ] , ’ psc2 ’ ) ;
saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{ i chant }’ iSegm ’ num2str ( iSegmentTrajectory ) ’ f i ltSPECT mic ’ s t r type { type}’ mic=’ num2str ( [ imic ] ) ’ . f i g ’ ] ) ;
end ;end ;c l o s e a l l ;
end ;end ;save ( [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{ i chant } ’ iSegm ’ num2str (
end ;%JAN load ( [ globDirOUT globEXTpar .OUTsub ’30m ’ globEXTcourt{ i chant } ’ IM .mat ’ ] ) ;load ( [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{ i chant } ’ iSegm ’ num2str (
iSegmentTrajectory ) ’ IM .mat ’ ] ) ;
f o r C 2Dspect2DcorTDiFD=[1]de l tb=tb−tb ( round ( length ( tb ) /2) ) ;d e l f b=fb−fb ( round ( length ( fb ) /2) ) ;f o r imic=1:Nmic
f o r jmic=1: imic−1f o r type=1:3
dfmaxs{type , imic , jmic}=zero s ( l ength ( indt min ) ,Nmax) ;dtmaxs{type , imic , jmic}=zero s ( l ength ( indt min ) ,Nmax) ;PercentShow (0 , 1 , ’ xcorr2 dIM . . . ’ , [ ] , f a l s e ) ;f o r i t =1: l ength ( vecTimesTracking )
PercentShow ( i t , l ength ( vecTimesTracking ) , [ da t e s t r ( c l o ck ) ’ xcorr2 dIMfo r ’ globEXTcourt{ i chant } ’ type ’ num2str ( type ) ] , [ ] , t rue ) ;
dIM{ type , imic , jmic}=xcorr2 (IM{ type , imic , i t } ,IM{ type , jmic , i t }) ;maximax=−2∗max(max(dIM{type , imic , jmic }) )∗ones (1 , 3 ) ;dIMcurr=dIM{ type , imic , jmic } ;d f v o i s i n=round ( s i z e ( dIMcurr , 1 ) /30) ;d t vo i s i n=round ( s i z e ( dIMcurr , 2 ) /30) ;f o r imax=1:Nmax
maximax( imax )=max(max( dIMcurr ) ) ;[ fdIM{imax , type } , tdIM{imax , type}]= f i nd ( dIMcurr==maximax( imax ) ) ;f o r i d f=−d f v o i s i n : d f v o i s i nf o r i d t=−d tvo i s i n : d t vo i s i ncurd f=fdIM{imax , type}+id f ;curdt=tdIM{imax , type}+id t ;i f curdf>=1 && curdf<=s i z e ( dIMcurr , 1 ) && curdt>=1 && curdt<=s i z e (
dIMcurr , 2 )dIMcurr ( fdIM{imax , type}+id f , tdIM{imax , type}+id t )=−2∗maximax( imax ) ;endend ;end ;
end ;f o r imax=1:Nmax
percfdDV{imax , type}=100∗ fdIM{imax , type}/ s i z e (dIM{type , imic , jmic } ,1) ;perctdDV{imax , type}=100∗tdIM{imax , type}/ s i z e (dIM{type , imic , jmic } ,2) ;d o l f=fdIM{imax , type}/ s i z e (dIM{type , imic , jmic } ,1) ;do l t=tdIM{imax , type}/ s i z e (dIM{type , imic , jmic } ,2) ;herzfdDV{imax , type}=de l f b ( round ( do l f ∗ l ength ( de l f b ) ) ) ;timetdDV{imax , type}=de l tb ( round ( do l t ∗ l ength ( de l tb ) ) ) ;dfmaxs{type , imic , jmic }( i t , imax )=herzfdDV{imax , type } ;dtmaxs{type , imic , jmic }( i t , imax )=timetdDV{imax , type } ;dfmaxs{type , jmic , imic }( i t , imax )=−herzfdDV{imax , type } ;dtmaxs{type , jmic , imic }( i t , imax )=−timetdDV{imax , type } ;
end ;end ;
end ;end ;
end ;save ( [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{ i chant } ’ iSegm ’ num2str (
iSegmentTrajectory ) ’ dIM .mat ’ ] , ’ dfmaxs ’ , ’ dtmaxs ’ , ’ dIM ’ , ’ fdIM ’ , ’ tdIM ’ , ’ deltb ’ , ’ de l fb’ ) ;
save ( [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{ i chant } ’ iSegm ’ num2str (
f o r C ou l i e rTresh =[1] %[999 0 . 0 01 ] %us ing dIMdisp ( [ da t e s t r (now) ’ C ou l i e rTresh=’ num2str ( C ou l i e rTresh ) ] ) ;f o r ism=1:Nmic
f o r jsm=1: ism−1f o r type=1:3
dfmaxsMall=mean( dfmaxs{ type , ism , jsm } ,1) ;dtmaxsMall=mean( dtmaxs{ type , ism , jsm } ,1) ;d fmaxsSal l=std ( dfmaxs{ type , ism , jsm } ,1) ;dtmaxsSal l=std ( dtmaxs{ type , ism , jsm } ,1) ;f o r i t =3: l ength ( indt min )−2
f o r imax=1:Nmaxi f dfmaxs{type , ism , jsm }( i t , imax )<dfmaxsMall ( imax )−dfmaxsSal l ( imax )∗
C oul i e rTresh | | dfmaxs{ type , ism , jsm }( i t , imax )>dfmaxsMall ( imax )+dfmaxsSal l ( imax )∗C oul i e rTresh
dfmaxsNeibs=(dfmaxs{ type , ism , jsm }( i t −1,imax )+dfmaxs{type , ism , jsm }( i t+1,imax )+dfmaxs{ type , ism , jsm }( i t −2,imax )+dfmaxs{type , ism , jsm }( i t+2,imax ) ) /4 ;
dfmaxsMaxsDist=abs ( dfmaxs{ type , ism , jsm }( i t , : )−dfmaxsNeibs ) ;dfmaxsMaxs=dfmaxs{type , ism , jsm }( i t , f i nd ( dfmaxsMaxsDist==min(
dfmaxsMaxsDist ) , 1 , ’ f i r s t ’ ) ) ;dfmaxs{type , ism , jsm }( i t , imax )=(dfmaxsNeibs+dfmaxsMaxs ) /2 ;end ;i f dtmaxs{type , ism , jsm }( i t , imax )<dtmaxsMall ( imax )−dtmaxsSal l ( imax )∗
C oul i e rTresh | | dtmaxs{ type , ism , jsm }( i t , imax )>dtmaxsMall ( imax )+dtmaxsSal l ( imax )∗C oul i e rTresh
dtmaxsNeibs=(dtmaxs{ type , ism , jsm }( i t −1,imax )+dtmaxs{type , ism , jsm }( i t+1,imax )+dtmaxs{ type , ism , jsm }( i t −2,imax )+dtmaxs{type , ism , jsm }( i t+2,imax ) ) /2 ;
dtmaxsMaxsDist=abs ( dtmaxs{ type , ism , jsm }( i t , : )−dtmaxsNeibs ) ;dtmaxsMaxs=dtmaxs{type , ism , jsm }( i t , f i nd ( dtmaxsMaxsDist==min(
dtmaxsMaxsDist ) , 1 , ’ f i r s t ’ ) ) ;dtmaxs{type , ism , jsm }( i t , imax )=(dtmaxsNeibs+dtmaxsMaxs ) /2 ;end ;
end ;f o r zz=vecPlotZoomsOfCorrs
f i gu r e ,imagesc ( deltb , de l fb , dIM{type , ism , jsm }) ;ax i s xy ; t i t l e ( [ s t r type { type} ’ d f=’ num2str ( [ dfmaxs{ type , ism , jsm }(
i t , : ) ] ) ’ dt=’ num2str ( [ 0 . 0 1∗ round (100∗dtmaxs{ type , ism , jsm }( i t, : ) ) ] ) ] , ’ FontSize ’ , f s i z e ) ;
y l ab e l ( ’ Frequences Hz ’ , ’ FontSize ’ , f s i z e ) ; x l ab e l ( ’Temps en s ’ , ’FontSize ’ , f s i z e )
colormap ( f l i p ud ( gray ) ) ; zoom( zz ) ;begde l fb=1+round ( length ( de l f b ) /2)−round ( length ( de l f b ) /2/ zz ) ;begde l tb=1+round ( length ( de l tb ) /2)−round ( length ( de l tb ) /2/ zz ) ;endde l fb=round ( length ( de l f b ) /2)+round ( length ( de l f b ) /2/ zz )−1;enddeltb=round ( length ( de l tb ) /2)+round ( length ( de l tb ) /2/ zz )−1;i f l abe l 0 range1 s imp l e==0y l abe l ( [ num2str (0 .01∗ round (100∗ de l f b ( begde l fb ) ) ) ’ < Freq ,Hz < ’
num2str (0 .01∗ round (100∗ de l f b ( endde l fb ) ) ) ] , ’ FontSize ’ , f s i z e ) ;x l ab e l ( [ num2str (0 .01∗ round (100∗ de l tb ( begde l tb ) ) ) ’ < Temps , s < ’
num2str (0 .01∗ round (100∗ de l tb ( enddeltb ) ) ) ] , ’ FontSize ’ , f s i z e ) ;e l s ey l ab e l ( [ ’ Freq ,Hz ’ ] , ’ FontSize ’ , f s i z e ) ;x l ab e l ( [ ’ Temps , s ’ ] , ’ FontSize ’ , f s i z e ) ;end ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{
i chant } ’ iSegm ’ num2str ( iSegmentTrajectory ) ’ ’ s t r type { type}’ t =’ num2str ( i t ) ’ SMOOTH mic1 , mic2 , thresh= ’ num2str ( [ ism jsmC oul i e rTresh ] ) ’ zoom ’ num2str ( zz ) ’ . jpg ’ ] ) ;
saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{i chant } ’ iSegm ’ num2str ( iSegmentTrajectory ) ’ ’ s t r type { type}
15. APPENDIX4: MATLAB CODE FOR SIMULATION 107
’ t =’ num2str ( i t ) ’ SMOOTH mic1 , mic2 , thresh= ’ num2str ( [ ism jsmC oul i e rTresh ] ) ’ zoom ’ num2str ( zz ) ’ . jpg ’ ] ) ;
saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{i chant } ’ iSegm ’ num2str ( iSegmentTrajectory ) ’ ’ s t r type { type}’ t =’ num2str ( i t ) ’ SMOOTH mic1 , mic2 , thresh= ’ num2str ( [ ism jsmC oul i e rTresh ] ) ’ zoom ’ num2str ( zz ) ’ . jpg ’ ] ) ;
c l o s e a l l ;end ;
end ;end ;
end ;end ;%−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−f o r C plotDopDelorigIsmooth =[ ]
d i sp ( [ da t e s t r (now) ’ C plotDopDelorigIsmooth=’ num2str ( C plotDopDelorigIsmooth ) ] ) ;f o r i p l =1:Nmic
%LineSty l eS t r ={ ’− ’ , ’−− ’ , ’− . ’ , ’ : ’} ; MarkerStr={ ’o ’ , ’ d ’ , ’+ ’ , ’ x ’ } ;f o r j p l =1: ip l −1
f o r type=1:3s c r s z=get (0 , ’ ScreenSize ’ ) ;f i g u r e ( ’ Pos i t ion ’ , [ 0 0 s c r s z (3 ) s c r s z (4 ) ] ) ;subplot (2 , 3 , 1 ) ; hold on ;f o r jmax=1:Nmax
p lo t ( l i n s pa c e ( tsAllTime ( indt mid (1) ) , tsAllTime ( indt mid ( end ) ) , s i z e (dfmaxs{ type , i p l , j p l } ,1) ) , dfmaxs{ type , i p l , j p l } ( : , jmax ) , ’ LineWidth’ , 2 , ’ L ineSty le ’ , L ineSty l eS t r {1 , jmax } , ’ MarkerSize ’ , msize , ’ Marker’ , MarkerStr {1 , jmax}) ;% . . .
%p lo t ( l i n s pa c e ( tsAllTime ( indt mid (1) ) , tsAllTime ( indt mid ( end ) ) , s i z e (dfmaxs{ type , i p l , j p l } ,1) ) , dfmaxs{ type , i p l , j p l } ( : , 2 ) , ’ LineWidth’ , 2 , ’ L ineSty le ’ , ’−− ’ , ’ MarkerSize ’ , msize , ’ Marker ’ , ’ d ’ ) ;% . . .
%p lo t ( l i n s pa c e ( tsAllTime ( indt mid (1) ) , tsAllTime ( indt mid ( end ) ) , s i z e (dfmaxs{ type , i p l , j p l } ,1) ) , dfmaxs{ type , i p l , j p l } ( : , 3 ) , ’ LineWidth’ , 2 , ’ L ineSty le ’ , ’ − . ’ , ’ MarkerSize ’ , msize , ’ Marker ’ , ’+ ’ ) ;% . . .
%p lo t ( l i n s pa c e ( tsAllTime ( indt mid (1) ) , tsAllTime ( indt mid ( end ) ) , s i z e (dfmaxs{ type , i p l , j p l } ,1) ) , dfmaxs{ type , i p l , j p l } ( : , 4 ) , ’ LineWidth’ , 2 , ’ L ineSty le ’ , ’ : ’ , ’ MarkerSize ’ , msize , ’ Marker ’ , ’ x ’ ) ;% . . .
end ;i f l abe l 0 range1 s imp l e==0
x l abe l ( [ num2str (0 .01∗ round (100∗ tsAllTime ( indt mid (1) ) ) ) ’ < Temps , s< ’ num2str (0 .01∗ round (100∗ tsAllTime ( indt mid ( end ) ) ) ) ] , ’FontSize ’ , f s i z e ) ;
y l ab e l ( [ num2str (0 .01∗ round (100∗min( dfmaxs{ type , i p l , j p l } ( 1 , : ) ) ) ) ’ <Hz( ends )< ’ num2str (0 .01∗ round (100∗max( dfmaxs{ type , i p l , j p l }( end, : ) ) ) ) ] , ’ FontSize ’ , f s i z e ) ;
e l s ex l ab e l ( [ ’ Temps , s ’ ] , ’ FontSize ’ , f s i z e ) ;y l ab e l ( [ ’ Hz( ends ) ’ ] , ’ FontSize ’ , f s i z e ) ;
end ;t i t l e ( ’ Doppler e f f e c t a l l maxs ’ , ’ FontSize ’ , f s i z e ) ;f o r imax=1:min (2 ,Nmax)
subplot (2 ,3 ,1+ imax ) ;p l o t ( l i n s pa c e ( tsAllTime ( indt mid (1) ) , tsAllTime ( indt mid ( end ) ) , s i z e (
dfmaxs{ type , i p l , j p l } ,1) ) , dfmaxs{ type , i p l , j p l } ( : , imax ) , ’ LineWidth’ , 2 , ’ L ineSty le ’ , L ineSty l eS t r { imax } , ’ MarkerSize ’ , msize , ’ Marker ’ ,MarkerStr{ imax}) ;% . . .
%legend ( [ ’max ’ num2str ( imax ) ] , ’ Location ’ , ’ Best ’ , ’ FontSize ’ , f s i zeSm ) ;i f l abe l 0 range1 s imp l e==0ax i s ( [ min ( tsAllTime ( indt mid ) ) max( tsAllTime ( indt mid ) ) mean( dfmaxs{
type , i p l , j p l } ( : , imax ) )−std ( dfmaxs{type , i p l , j p l } ( : , imax ) ) mean(dfmaxs{ type , i p l , j p l } ( : , imax ) )+std ( dfmaxs{ type , i p l , j p l } ( : , imax ) )] ) ;
x l ab e l ( [ num2str (0 .01∗ round (100∗ tsAllTime ( indt mid (1) ) ) ) ’ < Temps , s< ’ num2str (0 .01∗ round (100∗ tsAllTime ( indt mid ( end ) ) ) ) ] , ’FontSize ’ , f s i z e ) ;
y l ab e l ( [ num2str (0 .01∗ round (100∗min( dfmaxs{ type , i p l , j p l } ( : , imax ) ) ) ) ’< Hz < ’ num2str (0 .01∗ round (100∗max( dfmaxs{ type , i p l , j p l } ( : , imax) ) ) ) ] , ’ FontSize ’ , f s i z e ) ;
e l s ex l ab e l ( [ ’ Temps , s ’ ] , ’ FontSize ’ , f s i z e ) ;y l ab e l ( [ ’ Hz ’ ] , ’ FontSize ’ , f s i z e ) ;end ;t i t l e ( [ ’ Doppler e f f e c t max ’ num2str ( imax ) ] , ’ FontSize ’ , f s i z e ) ;
end ;subplot (2 , 3 , 4 ) ; hold on ;f o r jmax=1:Nmax
p lo t ( l i n s pa c e ( tsAllTime ( indt mid (1) ) , tsAllTime ( indt mid ( end ) ) , s i z e (dtmaxs{ type , i p l , j p l } ,1) ) , dtmaxs{ type , i p l , j p l } ( : , jmax ) , ’ LineWidth
108 BIBLIOGRAPHY
’ , 2 , ’ L ineSty le ’ , L ineSty l eS t r {1 , jmax } , ’ MarkerSize ’ , msize , ’ Marker’ , MarkerStr {1 , jmax}) ;% . . .
%p lo t ( l i n s pa c e ( tsAllTime ( indt mid (1) ) , tsAllTime ( indt mid ( end ) ) , s i z e (dtmaxs{ type , i p l , j p l } ,1) ) , dtmaxs{ type , i p l , j p l } ( : , 2 ) , ’ LineWidth’ , 2 , ’ L ineSty le ’ , ’−− ’ , ’ MarkerSize ’ , msize , ’ Marker ’ , ’ d ’ ) ;% . . .
%p lo t ( l i n s pa c e ( tsAllTime ( indt mid (1) ) , tsAllTime ( indt mid ( end ) ) , s i z e (dtmaxs{ type , i p l , j p l } ,1) ) , dtmaxs{ type , i p l , j p l } ( : , 3 ) , ’ LineWidth’ , 2 , ’ L ineSty le ’ , ’ − . ’ , ’ MarkerSize ’ , msize , ’ Marker ’ , ’+ ’ ) ;% . . .
%p lo t ( l i n s pa c e ( tsAllTime ( indt mid (1) ) , tsAllTime ( indt mid ( end ) ) , s i z e (dtmaxs{ type , i p l , j p l } ,1) ) , dtmaxs{ type , i p l , j p l } ( : , 4 ) , ’ LineWidth’ , 2 , ’ L ineSty le ’ , ’ : ’ , ’ MarkerSize ’ , msize , ’ Marker ’ , ’ x ’ ) ;% . . .
end ;i f l abe l 0 range1 s imp l e==0
x l abe l ( [ num2str (0 .01∗ round (100∗ tsAllTime ( indt mid (1) ) ) ) ’ < Temps , s< ’ num2str (0 .01∗ round (100∗ tsAllTime ( indt mid ( end ) ) ) ) ] , ’FontSize ’ , f s i z e ) ;
y l ab e l ( [ num2str (0 .01∗ round (100∗min( dtmaxs{ type , i p l , j p l } ( 1 , : ) ) ) ) ’ <s( ends )< ’ num2str (0 .01∗ round (100∗max( dtmaxs{ type , i p l , j p l }( end , : )) ) ) ] , ’ FontSize ’ , f s i z e ) ;
e l s ex l ab e l ( [ ’ Temps , s ’ ] , ’ FontSize ’ , f s i z e ) ;y l ab e l ( [ ’ s ( ends ) ’ ] , ’ FontSize ’ , f s i z e ) ;
end ;t i t l e ( ’ Delay a l l maxs ’ , ’ FontSize ’ , f s i z e ) ;f o r imax=1:min (2 ,Nmax)
subplot (2 ,3 ,4+ imax ) ;p l o t ( l i n s pa c e ( tsAllTime ( indt mid (1) ) , tsAllTime ( indt mid ( end ) ) , s i z e (
dtmaxs{ type , i p l , j p l } ,1) ) , dtmaxs{ type , i p l , j p l } ( : , imax ) , ’ LineWidth’ , 2 , ’ L ineSty le ’ , L ineSty l eS t r { imax } , ’ MarkerSize ’ , msize , ’ Marker ’ ,MarkerStr{ imax}) ;% . . .
%legend ( [ ’max ’ num2str ( imax ) ] , ’ Location ’ , ’ Best ’ , ’ FontSize ’ , f s i zeSm ) ;i f l abe l 0 range1 s imp l e==0ax i s ( [ min ( tsAllTime ( indt mid ) ) max( tsAllTime ( indt mid ) ) mean( dtmaxs{
type , i p l , j p l } ( : , imax ) ) −0.3∗ std ( dtmaxs{ type , i p l , j p l } ( : , imax ) )mean( dtmaxs{type , i p l , j p l } ( : , imax ) )+0.3∗ std ( dtmaxs{ type , i p l , j p l} ( : , imax ) ) ] ) ;
x l ab e l ( [ num2str (0 .01∗ round (100∗ tsAllTime ( indt mid (1) ) ) ) ’ < Temps , s< ’ num2str (0 .01∗ round (100∗ tsAllTime ( indt mid ( end ) ) ) ) ] , ’FontSize ’ , f s i z e ) ;
y l ab e l ( [ num2str (0 .01∗ round (100∗min( dtmaxs{ type , i p l , j p l } ( : , imax ) ) ) ) ’< s < ’ num2str (0 .01∗ round (100∗max( dtmaxs{type , i p l , j p l } ( : , imax )) ) ) ] , ’ FontSize ’ , f s i z e ) ;
e l s ex l ab e l ( [ ’ Temps , s ’ ] , ’ FontSize ’ , f s i z e ) ;y l ab e l ( [ ’ s ’ ] , ’ FontSize ’ , f s i z e ) ;end ;t i t l e ( [ ’ Delay max ’ num2str ( imax ) ] , ’ FontSize ’ , f s i z e ) ;
end ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{ i chant }
’ iSegm ’ num2str ( iSegmentTrajectory ) ’DopDel ’ s t r type { type} ’ mic1 ,mic2 , thresh= ’ num2str ( [ i p l j p l C ou l i e rTresh ] ) ’ . jpg ’ ] ) ;
saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{ i chant }’ iSegm ’ num2str ( iSegmentTrajectory ) ’DopDel ’ s t r type { type} ’ mic1 ,mic2 , thresh= ’ num2str ( [ i p l j p l C ou l i e rTresh ] ) ’ . f i g ’ ] ) ;
saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{ i chant }’ iSegm ’ num2str ( iSegmentTrajectory ) ’DopDel ’ s t r type { type} ’ mic1 ,mic2 , thresh= ’ num2str ( [ i p l j p l C ou l i e rTresh ] ) ’ . eps ’ ] , ’ psc2 ’ ) ;
c l o s e a l l ;end
endend ;
end ;%−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−
end ;end ;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% LIKEHOOD %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%fo r C likehood2D =[]
d i sp ( [ da t e s t r (now) ’ C likehood2D C likehood2D C likehood2D LIKEHOOD’ ] ) ;ca lcLikehood=1;% indf min=20; %25% indf max=85; %80% indt min= 900−(−1)∗200:2∗60:2300+(−1) ∗200;% indt max=1100−(−1)∗200:2∗60:2500+(−1) ∗200;delXY=maxXYZ/3 ;delV=(maxVel−minVel ) /6 ;
15. APPENDIX4: MATLAB CODE FOR SIMULATION 109
vecXY=0.5∗delXY : delXY :3∗maxXYZ; NvecXY=length (vecXY) ;vecV=minVel : delV : maxVel ; NvecV=length ( vecV ) ;a r r i v e d f s=c e l l (NvecXY ,NvecXY ,NvecV ,NvecV , Nmic) ;a r r i v e d t s=c e l l (NvecXY ,NvecXY ,NvecV ,NvecV , Nmic) ;a r r i v edps=c e l l (NvecXY ,NvecXY ,NvecV ,NvecV , Nmic) ;a r r i v edy s=c e l l (NvecXY ,NvecXY ,NvecV ,NvecV , Nmic) ;a l l c o r r=c e l l (NvecXY ,NvecXY ,NvecV ,NvecV ,Nmic , Nmic) ;l i kehood=c e l l (NvecXY ,NvecXY ,NvecV ,NvecV) ;f o r i s e n s =1:Nmic
f o r ix =1:NvecXY ,f o r iy =1:NvecXY
fo r ivx=1:NvecV ,f o r ivy=1:NvecV
l ikehood { ix , iy , ivx , ivy}=zero s (NvecXY ,NvecXY ,NvecV ,NvecV , Nmic) ;end ;
end ;end ;
end ;end ;
ivar iantMic =1;%load ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTpath0{ i chant } ’ALL.
wav a l l v a r s .mat ’ ] ) ;load ( [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{ i chant } ’ IM .mat ’ ] ) ;f o r i t =1: l ength ( vecTimesTracking )
i f ca lcLikehood==1f o r ix =1:NvecXY , [ da t e s t r (now) ’ ix=’ num2str ( ix ) ]
f o r iy =1:NvecXYfo r ivx=1:NvecV ,
f o r ivy=1:NvecV%load ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( [ i var iantMic ] )
’ ’ globEXTcourt{ i chant } ’Time=’ num2str ( [ indt min ( i t ) indt max (i t ) ] ) ’ . mat ’ ] ) ;
ps=psAllTime ( indf min : indf max , indt min ( i t ) : indt max ( i t ) ) ;ys=ysAllTime ( indf min : indf max , indt min ( i t ) : indt max ( i t ) ) ;f s=fsAl lTime ( indf min : indf max ) ;t s=tsAllTime ( indt min ( i t ) : indt max ( i t ) ) ;f o r i s e n s =1:Nmicvec tosens=sensS1XY2 ( i s ens , : ) −[vecXY( ix ) vecXY( iy ) 0 ] ;ab sd i s t t o s en s=sq r t ( squeeze ( vec to sens )∗ squeeze ( vec to sens ) ’ ) ;dsec=ab sd i s t t o s en s /c ;dsec IndStar t=round ( dsec /mean( d i f f ( t s ) ) ) ; dsecIndLen=length ( t s )−abs (
dsec IndStar t )−1;i f dsecIndLen <0, e r r o r ( ’ dsecIndLen<0 : a r r i v a l dsec too big :
d i f f e r e n t s en so r s r e c e i v e d i f f e r e n t subspectrograms ’ ) ; end ;i f dsecIndStart >0newtsvec=dsec IndStar t : dsec IndStar t+dsecIndLen ;o ld t sv e c=1:1+dsecIndLen ;e l s enewtsvec=1:1+dsecIndLen ;o ld t sv e c=1+abs ( dsec IndStar t ) :1+abs ( dsec IndStar t )+dsecIndLen ;end
ve l t o s en s =[vecV ( ivx ) vecV ( ivy ) 0 ]∗ vectosens ’ / ab sd i s t t o s en s ;d f r eq=−( f r eq0 /c )∗ ve l t o s en s ;d f r eq IndSta r t=round ( d f r eq /mean( d i f f ( f s ) ) ) ; dfreqIndLen=length ( f s )−
abs ( d f r eq IndSta r t )−1;i f dfreqIndLen <0, e r r o r ( ’ dfreqIndLen<0 : a r r i v a l d f r eq too big : out
o f subspectrogram ’ ) ; end ;i f d f reqIndStart >0newfsvec=df r eq IndSta r t : d f r eq IndSta r t+dfreqIndLen ;o l d f s v e c=1:1+dfreqIndLen ;e l s enewfsvec=1:1+dfreqIndLen ;o l d f s v e c=1+abs ( d f r eq IndSta r t ) :1+abs ( d f r eq IndSta r t )+dfreqIndLen ;enda r r i v e d f s { ix , iy , ivx , ivy , i s e n s}=f s+df req ;a r r i v e d t s { ix , iy , ivx , ivy , i s e n s}=ts+dsec ;a r r i v edps { ix , iy , ivx , ivy , i s e n s}=ps ;%ar r i v edps { ix , iy , ivx , ivy , i s e n s}=imnoise ( imnoise ( imnoise ( imnoise (
a r r i v edps { ix , iy , ivx , ivy , i s e n s }( newfsvec , newtsvec )=ps ( o ld f svec ,o l d t sv e c ) ;
%ar r i v edps { ix , iy , ivx , ivy , i s e n s }( newfsvec , newtsvec )=imnoise ( imnoise (imnoise ( imnoise ( a r r i v edps { ix , iy , ivx , ivy , i s e n s }( newfsvec , newtsvec) , ’ poisson ’ ) , ’ poisson ’ ) , ’ poisson ’ ) , ’ s a l t & pepper ’ , 0 . 0 2 ) ;
110 BIBLIOGRAPHY
a r r i v edys { ix , iy , ivx , ivy , i s e n s}=ys ; a r r i v edy s { ix , iy , ivx , ivy , i s e n s }(newfsvec , newtsvec )=ys ( o ld f svec , o l d t sv e c ) ;
end ;end ;
end ;end ;
end ;[’−−−−−−−−−−−−−−−−−−−−−− s t a r t l i kehood cyc l e −−−−−−−−−−−−−−−−−−−−−−− ’ d a t e s t r (now)
]f o r i s e n s =1:Nmic
f o r ix =1:NvecXY , [ da t e s t r (now) ’ ix=’ num2str ( ix ) ]f o r iy =1:NvecXY ,
f o r ivx=1:NvecV ,f o r ivy=1:NvecV ,f o r jx=ix +1:NvecXY ,f o r jy=iy +1:NvecXYfo r jvx=ivx+1:NvecV ,f o r jvy=ivy+1:NvecV%tmp=max(max( xcorr2 ( a r r i v edps { ix , iy , ivx , ivy , i s e n s } , a r r i v edps { jx , jy ,
jvx , jvy , i s e n s }) ) ) ;tmp=dot ( reshape ( a r r i v edps { ix , iy , ivx , ivy , i s e n s } , 1 , [ ] ) , reshape (
a r r i v edps { jx , jy , jvx , jvy , i s e n s } , 1 , [ ] ) ) ;l i kehood { ix , iy , ivx , ivy }( jx , jy , jvx , jvy , i s e n s )=tmp ;l ikehood { jx , jy , jvx , jvy }( ix , iy , ivx , ivy , i s e n s )=tmp ;%tmpend ;end ;end ;end ;end ;
end ;end ;
end ;end ;save ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( [ i t ] ) ’ ’ globEXTcourt{ i chant } ’
ArrivedToALLS .mat ’ ] , ’ l ikehood ’ , ’ a r r i v ed f s ’ , ’ a r r i v ed t s ’ , ’ a r r ivedps ’ , ’ a r r ivedys ’ ) ;e l s e
load ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( [ i t ] ) ’ ’ globEXTcourt{ i chant } ’ArrivedToALLS .mat ’ ] ) ;
end ;
i s e n s =1; ix=round (NvecXY/2) ; iy=round (NvecXY/2) ; ivx=round (NvecV/2) ; ivy=round (NvecV/2) ; jx=round (NvecXY/2) ; jy=round (NvecXY/2) ; jvx=round (NvecV/2) ; jvy=round (NvecV/2) ;
f o r i s e n s =1:Nmics c r s z=get (0 , ’ ScreenSize ’ ) ; f i g u r e ( ’ Pos i t ion ’ , [ 0 0 s c r s z (3 ) s c r s z (4 ) ] ) ; i p l o t =0;f o r ix =1:NvecXY ,
f o r iy =1:NvecXY ,i p l o t=i p l o t +1; subplot (NvecXY ,NvecXY , i p l o t ) ;imagesc ( a r r i v e d t s { ix , iy , ivx , ivy , i s e n s } , a r r i v e d f s { ix , iy , ivx , ivy , i s e n s } , l og10 (
abs ( a r r i v edps { ix , iy , ivx , ivy , i s e n s }) ) ) ;colormap ( f l i p ud ( gray ) ) ; ax i s o f f ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{ i chant }
num2str ( [ i t ] ) ’ spectToS ’ num2str ( i s e n s ) ’XYw4m. jpg ’ ] ) ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{ i chant }
num2str ( [ i t ] ) ’ spectToS ’ num2str ( i s e n s ) ’XYw4m. eps ’ ] , ’ psc2 ’ ) ;end ;
end ;c l o s e a l l
end ;
i s e n s =1; ix=round (NvecXY/2) ; iy=round (NvecXY/2) ; ivx=round (NvecV/2) ; ivy=round (NvecV/2) ; jx=round (NvecXY/2) ; jy=round (NvecXY/2) ; jvx=round (NvecV/2) ; jvy=round (NvecV/2) ;
f o r i s e n s =1:Nmics c r s z=get (0 , ’ ScreenSize ’ ) ; f i g u r e ( ’ Pos i t ion ’ , [ 0 0 s c r s z (3 ) s c r s z (4 ) ] ) ; i p l o t =0;f o r ix =1:NvecXY ,
f o r iy =1:NvecXY ,i p l o t=i p l o t +1; subplot (NvecXY ,NvecXY , i p l o t ) ;imagesc ( squeeze ( l ikehood { ix , iy , ivx , ivy } ( : , : , jvx , jvy , i s e n s ) ) ) ;ax i s o f f ; colormap ( f l i p ud ( gray ) ) ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{ i chant }
num2str ( [ i t ] ) ’ spectToS ’ num2str ( i s e n s ) ’XY−Likehoodw4m . jpg ’ ] ) ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{ i chant }
num2str ( [ i t ] ) ’ spectToS ’ num2str ( i s e n s ) ’XY−Likehoodw4m . eps ’ ] , ’ psc2 ’ ) ;end ;
end ;c l o s e a l l
end ;
15. APPENDIX4: MATLAB CODE FOR SIMULATION 111
i s e n s =1; ix=round (NvecXY/2) ; iy=round (NvecXY/2) ; ivx=round (NvecV/2) ; ivy=round (NvecV/2) ; jx=round (NvecXY/2) ; jy=round (NvecXY/2) ; jvx=round (NvecV/2) ; jvy=round (NvecV/2) ;
s c r s z=get (0 , ’ ScreenSize ’ ) ; f i g u r e ( ’ Pos i t ion ’ , [ 0 0 s c r s z (3 ) s c r s z (4 ) ] ) ; i p l o t =0;f o r ix =[1 round (NvecXY/2) NvecXY ] ,
f o r iy =[1 round (NvecXY/2) NvecXY ] ,f o r i s e n s =1:Nmic
i p l o t=i p l o t +1; subplot ( 9 , (Nmic+1) , i p l o t ) ;imagesc ( a r r i v e d t s { ix , iy , ivx , ivy , i s e n s } , a r r i v e d f s { ix , iy , ivx , ivy , i s e n s } , l og10 (
abs ( a r r i v edps { ix , iy , ivx , ivy , i s e n s }) ) ) ;colormap ( f l i p ud ( gray ) ) ; ax i s o f f ;
end ;i p l o t=i p l o t +1; subplot ( 9 , (Nmic+1) , i p l o t ) ; imagesc ( squeeze ( l i kehood { ix , iy , ivx , ivy
} ( : , : , jvx , jvy , i s e n s ) ) ) ; ax i s o f f ;end ;
end ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{ i chant } num2str ( [ i t ] ) ’
a l lSpectToS ’ num2str ( i s e n s ) ’XY−Likehoodw4m . jpg ’ ] ) ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{ i chant } num2str ( [ i t ] ) ’
a l lSpectToS ’ num2str ( i s e n s ) ’XY−Likehoodw4m . eps ’ ] , ’ psc2 ’ ) ;c l o s e a l l ;
i s e n s =1; ix=round (NvecXY/2) ; iy=round (NvecXY/2) ; ivx=round (NvecV/2) ; ivy=round (NvecV/2) ; jx=round (NvecXY/2) ; jy=round (NvecXY/2) ; jvx=round (NvecV/2) ; jvy=round (NvecV/2) ;
f o r i s e n s =1:Nmics c r s z=get (0 , ’ ScreenSize ’ ) ; f i g u r e ( ’ Pos i t ion ’ , [ 0 0 s c r s z (3 ) s c r s z (4 ) ] ) ; i p l o t =0;f o r ivx=1:NvecV ,
f o r ivy=1:NvecV ,i p l o t=i p l o t +1; subplot (NvecV ,NvecV , i p l o t ) ;imagesc ( a r r i v e d t s { ix , iy , ivx , ivy , i s e n s } , a r r i v e d f s { ix , iy , ivx , ivy , i s e n s } , l og10 (
abs ( a r r i v edps { ix , iy , ivx , ivy , i s e n s }) ) ) ;colormap ( f l i p ud ( gray ) ) ; ax i s o f f ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{ i chant }
num2str ( [ i t ] ) ’ SpectToS ’ num2str ( i s e n s ) ’VxVyw4m. jpg ’ ] ) ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{ i chant }
num2str ( [ i t ] ) ’ SpectToS ’ num2str ( i s e n s ) ’VxVyw4m. eps ’ ] , ’ psc2 ’ ) ;end ;
end ;c l o s e a l l
end ;
i s e n s =1; ix=round (NvecXY/2) ; iy=round (NvecXY/2) ; ivx=round (NvecV/2) ; ivy=round (NvecV/2) ; jx=round (NvecXY/2) ; jy=round (NvecXY/2) ; jvx=round (NvecV/2) ; jvy=round (NvecV/2) ;
f o r i s e n s =1:Nmics c r s z=get (0 , ’ ScreenSize ’ ) ; f i g u r e ( ’ Pos i t ion ’ , [ 0 0 s c r s z (3 ) s c r s z (4 ) ] ) ; i p l o t =0;f o r ivx=1:NvecV ,
f o r ivy=1:NvecV ,i p l o t=i p l o t +1; subplot (NvecV ,NvecV , i p l o t ) ;imagesc ( squeeze ( l ikehood { ix , iy , ivx , ivy }( jx , jy , : , : , i s e n s ) ) ) ;max(max( l ikehood { ix , iy , ivx , ivy }( jx , jy , : , : , i s e n s ) ) )−min(min ( l ikehood { ix , iy ,
ivx , ivy }( jx , jy , : , : , i s e n s ) ) )ax i s o f f ; colormap ( f l i p ud ( gray ) ) ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{ i chant }
num2str ( [ i t ] ) ’ SpectToS ’ num2str ( i s e n s ) ’VxVy−Likehoodw4m . jpg ’ ] ) ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{ i chant }
num2str ( [ i t ] ) ’ SpectToS ’ num2str ( i s e n s ) ’VxVy−Likehoodw4m . eps ’ ] , ’ psc2 ’ ) ;end ;
end ;c l o s e a l l
end ;i s e n s =1; ix=round (NvecXY/2) ; iy=round (NvecXY/2) ; ivx=round (NvecV/2) ; ivy=round (NvecV/2) ; jx=
round (NvecXY/2) ; jy=round (NvecXY/2) ; jvx=round (NvecV/2) ; jvy=round (NvecV/2) ;s c r s z=get (0 , ’ ScreenSize ’ ) ; f i g u r e ( ’ Pos i t ion ’ , [ 0 0 s c r s z (3 ) s c r s z (4 ) ] ) ; i p l o t =0;f o r ivx=[1 round (NvecV/2) NvecV ] ,
f o r ivy=[1 round (NvecV/2) NvecV ] ,f o r i s e n s =1:Nmic
i p l o t=i p l o t +1; subplot ( 9 , (Nmic+1) , i p l o t ) ;imagesc ( a r r i v e d t s { ix , iy , ivx , ivy , i s e n s } , a r r i v e d f s { ix , iy , ivx , ivy , i s e n s } , l og10 (
abs ( a r r i v edps { ix , iy , ivx , ivy , i s e n s }) ) ) ;colormap ( f l i p ud ( gray ) ) ; ax i s o f f ;
end ;i p l o t=i p l o t +1; subplot ( 9 , (Nmic+1) , i p l o t ) ; imagesc ( squeeze ( l i kehood { ix , iy , ivx , ivy
}( jx , jy , : , : , i s e n s ) ) ) ; ax i s o f f ;end ;
end ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{ i chant } num2str ( [ i t ] ) ’
ALLSpectToS ’ num2str ( i s e n s ) ’VxVy−Likehoodw4m . jpg ’ ] ) ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le globEXTcourt{ i chant } num2str ( [ i t ] ) ’
112 BIBLIOGRAPHY
ALLSpectToS ’ num2str ( i s e n s ) ’VxVy−Likehoodw4m . eps ’ ] , ’ psc2 ’ ) ;c l o s e a l l ;
end ;end ;end%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% BEGIN CORRELATION OF SIGNALS AND REFINE TDOA TIME CALCULATION%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%fo r C cor =[0] %[−1 1 0 ]t i cd i sp ( [ da t e s t r (now) ’ C 1DcorTDOArefineIentropy C 1DcorTDOArefineIentropy CORRELATION OF SIGNALS
AND REFINE TDOA’ ] ) ;maxKHZ=100;plotTIME=c e l l ( nb f i c h i e r , l ength ( micro ) , l ength ( micro ) ) ;plotTDOA1=c e l l ( nb f i c h i e r , l ength ( micro ) , l ength ( micro ) ) ;plotTDOA=c e l l ( l ength ( vecALL percTDOArefine ) , nb f i c h i e r , l ength ( micro ) , l ength ( micro ) ) ;plotHmax1=c e l l ( nb f i c h i e r , l ength ( micro ) , l ength ( micro ) ) ;plotHmax=c e l l ( l ength ( vecALL percTDOArefine ) , nb f i c h i e r , l ength ( micro ) , l ength ( micro ) ) ;FFTindexKHZ=c e l l ( nb f i c h i e r , l ength ( micro ) ) ; dfmaxsSIG=c e l l ( s i z e ( dfmaxs ) ) ; dtmaxsSIG=c e l l ( s i z e (
dtmaxs ) ) ;f o r C INDpercTDOArefine=1: l ength ( vecALL percTDOArefine )
C percTDOArefine=vecALL percTDOArefine (C INDpercTDOArefine ) ;d e l a i = [ ] ; t d e l a i =0;micro gard=micro ;f o r imic=1: l ength ( micro gard )
FFTindexKHZ{ nb f i c h i e r , imic}=zero s ( l ength ( vecTimesTracking ) ,maxKHZ) ;f o r jmic=1: l ength ( micro gard )
f o r type=1:3dfmaxsSIG{ type , imic , jmic}=zero s ( l ength ( vecTimesTracking ) , nb td ) ;dtmaxsSIG{ type , imic , jmic}=zero s ( l ength ( vecTimesTracking ) , nb td ) ;
end ;end ;
end ;tr ALL=c e l l (1 , n b f i c h i e r ) ;tr AVER=c e l l (1 , n b f i c h i e r ) ;tr STD=c e l l (1 , n b f i c h i e r ) ;tr HISTx=c e l l ( nb f i c h i e r , nb td ) ;tr HISTy=c e l l ( nb f i c h i e r , nb td ) ;
%−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−f o r C ca lctdoa =[1]
d i sp ( [ da t e s t r (now) ’ C ca lctdoa C calctdoa C calctdoa ’ ] ) ;f o r i f i l e=ichant
tr ALL{ i f i l e }=[ ] ;PercentShow (0 , 1 , ’ ca l c tdoa . . . ’ , [ ] , f a l s e ) ; %h = waitbar ( 0 , [ ’ ca l c tdoa f o r ’
num2str ( i f i l e ) ] ) ;i n o f =0;f o r nof=vecTimesTracking
i n o f=i no f +1;PercentShow ( ino f , l ength ( vecTimesTracking ) , [ da t e s t r ( c l o ck ) ’ca l c tdoa f o r ’ globEXTcourt{ i f i l e } ] , [ ] , t rue ) ; %waitbar ( nof /( nb secMYmsec−startMYmsec ) ) ;
nofMid ind=nof∗ indINmsec ;no fS t a r t i nd=round ( nofMid ind−ovMYmsec∗ indINmsec ) ;nofEnd ind=round ( nofMid ind+ovMYmsec∗ indINmsec ) ;
i f datafrom0cyberio1dodo==0 % ichant<=57eva l ( [ ’ vNEIB=wavread ( [ globDirIN globEXTpath{ i f i l e } ] , [ nofStart indNEIB
nofEnd indNEIB ] ) ’ ’ ; ’ ] ) ;eva l ( [ ’ v=wavread ( [ globDirIN globEXTpath{ i f i l e } ] , [ n o f S t a r t i nd nofEnd ind ] )
’ ’ ; ’ ] ) ;e l s e
load ( [ globDirIN globEXTpar . INsubDODO globEXTpath{ i chant } ] ) ;vNEIB=tcu r de t ( nofStart indNEIB : nofEnd indNEIB , : ) ; v=tcu r de t ( no fS t a r t i nd :
nofEnd ind , : ) ;c l e a r t cu r de t ;
end ;i f dotk==1
vor i g=v ; c l e a r yy ;f o r i i =1:4 ; yy ( : , i i ) = ( [ v ( i i , : ) 0 0 ] .∗ [ 0 v ( i i , : ) . ˆ2 0 ] .∗ [ 0 0 v ( i i
, : ) ] ) ; endv=yy ’ ; c l e a r v2 ;
15. APPENDIX4: MATLAB CODE FOR SIMULATION 113
% fo r i i =1:4 ; v2 ( : , i i ) = conv ( [ 1 1 1 1 1 1 1 11 ] , v ( i i , : ) ) ; end ;
% v = v2 ’ ;e l s e i f dotk==2
vor i g=v ; c l e a r yy ;cut = 0 . 7 5 ; % cut a l l below 0.75∗FS/2[B,A] = BUTTER(6 , cut , ’ high ’ ) ; %des i gns a highpass f i l t e r .y = f i l t e r (B,A, v ) ;v= y ;
end ;nofStart ms=no fS t a r t i nd /indVms ;nofEnd ms=nofEnd ind/indVms ;nofMid ms=nofMid ind /indVms ;tdoa2ca l c=c e l l (1 , l ength ( cortype ) ) ; CorrOrderStr=c e l l (1 , Nraz ) ;f o r i i =1: l ength ( cortype ) , tdoa2ca l c { i i }=[ ] ; end ;CorrOrderStr {1}=’ Cor r e l a t i on o f r e co rd ing s ’ ; CorrOrderStr{2}=’Peaks o f
c o r r e l a t i o n ’ ;% ETAPE CALCUL TDOA%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%fo r i i=C cortype
i f plotCORiTDOA==1s c r s z=get (0 , ’ ScreenSize ’ ) ;hcor=f i g u r e ( ’ Pos i t ion ’ , [ 0 0 s c r s z (3 ) s c r s z (4 ) ] ) ;
end ;i s ubp l o t =0; Ni j=( length ( micro gard )−1)∗ l ength ( micro gard ) /2 ;f o r i =1: l ength ( micro gard )−1
f o r j=i +1: l ength ( micro gard )i f i i <=3tdoa2ca l c { i i }=[ tdoa2ca l c { i i } ; ones ( l ength ( dfmaxsSIG{ i i , i , j }( ino f , : ) )
,1 ) ∗ [ i f i l e nof ] ones ( l ength ( dfmaxsSIG{ i i , i , j }( ino f , : ) ’ ) , 1 ) ∗ [micro gard ( i ) micro gard ( j ) ] dfmaxsSIG{ i i , i , j }( ino f , : ) ’ 0∗ (1 :nb td ) ’ ] ;
e l s e i f i i <=3∗2tdoa2ca l c { i i }=[ tdoa2ca l c { i i } ; ones ( l ength ( dtmaxsSIG{ i i −3, i , j }( ino f
, : ) ) ,1 ) ∗ [ i f i l e nof ] ones ( l ength ( dtmaxsSIG{ i i −3, i , j }( ino f , : ) ’ ) , 1 )∗ [ micro gard ( i ) micro gard ( j ) ] dtmaxsSIG{ i i −3, i , j }( ino f , : ) ’0∗ (1 : nb td ) ’ ] ;
e l s evNEIBj=vNEIB( j , : ) ;v i=v( i , : ) ;LvNEIB=length (vNEIBj ) ; Lv=length ( v i ) ; minL=min ( [ LvNEIB Lv ] ) ;i f c a l cFFTip lo t i t==1NFFT = 2ˆnextpow2 (minL) ;f i g u r e ; hgc f=gc f ; hgca=gca ;
curFFTindexKHZ=c e l l ( 1 , 3 ) ;f o r i subp l =1:3subplot (3 ,1 , i s ubp l ) ; hold on ;t i t l e s t r =[ ’AmplitudeAtTime ’ num2str ( nof ) ’ms−micro ’ ] ;i f i s ubp l==1i j s i g=v( i , : ) ’ ; i j=i ;t i t l e ( [ t i t l e s t r num2str ( i ) ’ with average ’ ] , ’ FontSize ’ , f s i z e ) ;e l s e i f i s ubp l==2i j s i g=vNEIB( j , : ) ’ ; i j=j ;t i t l e ( [ t i t l e s t r num2str ( j ) ’ with average ’ ] , ’ FontSize ’ , f s i z e ) ;e l s ei j s i g=v( i , : ) ’−vNEIB( j , : ) ’ ;t i t l e ( [ t i t l e s t r ’DIF ’ num2str ( i ) num2str ( j ) ’ with average ’ ] , ’
FontSize ’ , f s i z e ) ;%i j s i g=cc ( round (minL/2)+1: round (minL/2)+minL) ;%t i t l e ( [ t i t l e s t r num2str ( i ) num2str ( j ) ’ c o r r e l a t i on ’ ’ with average
’ ] , ’ FontSize ’ , f s i z e ) ;end ;vFFT = f f t ( i j s i g ,NFFT)/minL ;absvFFT=2∗abs (vFFT( 1 :NFFT/2+1) ) ;fFFT = f e /2∗ l i n s p a c e (0 ,1 ,NFFT/2+1) ;FFTindexKHZ{ ichant , i j }( ino f , : )=ze ro s (1 ,maxKHZ) ;curFFTindexKHZ{ i s ubp l }=[ ] ;f o r iKHZ=1:maxKHZcurindFFT=f ind ( fFFT>(iKHZ−0.9) ∗1000 & fFFT<(iKHZ+0.9) ∗1000 ) ;curFFTindexKHZ{ i s ubp l }=[curFFTindexKHZ{ i s ubp l } mean(absvFFT(
curindFFT ) ) ] ;end ;i f i subpl <=2, FFTindexKHZ{ ichant , i j }( ino f , : )=curFFTindexKHZ{ i s ubp l } ;
end ;p l o t ( fFFT(fFFT<maxKHZ∗1000) /1000 ,absvFFT(fFFT<maxKHZ∗1000) , ’m: ’ ) ;maxFFTindexKHZ( i subp l )=max( curFFTindexKHZ{ i s ubp l }) ;i f i subpl<=2
114 BIBLIOGRAPHY
p lo t ( 1 :maxKHZ, curFFTindexKHZ{ i s ubp l } , ’ k− ’) ;yl im ( [ 0 3∗maxFFTindexKHZ(1) ] ) ;e l s ep lo t ( 1 :maxKHZ, abs (FFTindexKHZ{ ichant , i }( ino f , : )−FFTindexKHZ{ ichant , j
}( ino f , : ) ) , ’ b− ’) ;p l o t ( 1 :maxKHZ, curFFTindexKHZ{ i s ubp l } , ’ k− ’) ;p l o t ( 1 :maxKHZ,0 . 0001∗ l og10 ( abs (FFTindexKHZ{ ichant , i }( ino f , : )−
FFTindexKHZ{ ichant , j }( ino f , : ) ) ) , ’ b−−’) ;p l o t ( 1 :maxKHZ,0 . 0001∗ l og10 ( curFFTindexKHZ{ i s ubp l }) , ’ k−−’) ;yl im ([−0 2∗maxFFTindexKHZ(3) ] ) ;end ;x l ab e l ( [ ’ kHz ’ ] , ’ FontSize ’ , f s i z e ) ;y l ab e l ( [ ’ amplitude ’ ] , ’ FontSize ’ , f s i z e ) ;l egend ( ’ Amplitude ’ , ’ Average ’ ) ;end ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor )
globEXTcourt{ i f i l e } ’ ’ t i t l e s t r num2str ( i ) num2str ( j ) ’ ’cortype { i i } ’ . jpg ’ ] ) ;
saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor )globEXTcourt{ i f i l e } ’ ’ t i t l e s t r num2str ( i ) num2str ( j ) ’ ’cortype { i i } ’ . eps ’ ] , ’ psc2 ’ ) ;
saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor )globEXTcourt{ i f i l e } ’ ’ t i t l e s t r num2str ( i ) num2str ( j ) ’ ’cortype { i i } ’ . f i g ’ ] ) ;
c l o s e ( gc f ) ;end ;i f TDOA0FD123type<0v i=in t e rp1 ( 1 :maxKHZ, curFFTindexKHZ{1} , l i n s p a c e (1 ,maxKHZ, Lv) ) ;vNEIBj=int e rp1 ( 1 :maxKHZ, curFFTindexKHZ{2} , l i n s p a c e (1 ,maxKHZ,LvNEIB) )
;cc=xcorr ( v i ( 1 : minL) /mean( v i ( 1 : minL) ) , vNEIBj ( 1 : minL) /mean(vNEIBj ( 1 :
minL) ) , StrCors ) ; %cc=xcorr (v ( j , : ) , v ( i , : ) , StrCors ) ; % ; SELON LAVERSION DE MATLAB ! ! !
cc=cc / cc ( round ( length ( cc ) /2) ) ;%−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−
%−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−f o r raz=1:C cortype −3∗2 %Nraz[ t r ordre ]= so r t ( cc ) ;ordre=ordre ( l ength ( cc ) : −1:1) ; t r=t r ( l ength ( cc ) : −1:1) ;ordre=ordre ∗4∗windowSize∗ovMYmsec/ length ( cc ) − 2∗ovMYmsec ;f o r k=1: nb tdordre1=ordre ;ordre=ordre ( [ 1 : k k+f ind ( abs ( ordre (k+1: l ength ( ordre ) )−ordre (k ) )>
e p r a i e ) ] ) ;t r=t r ( [ 1 : k k+f ind ( abs ( ordre1 (k+1: l ength ( ordre1 ) )−ordre1 (k ) )>e p r a i e )
] ) ;end ;tr ALL{ i f i l e }=[tr ALL{ i f i l e } ; t r ( 1 : nb td ) ] ;ordre temp=ordre ( 1 : nb td ) ;%MY ordre temp=ordre temp ( f i nd ( ordre temp<sq r t (sum( ( pos mic (
micro gard ( i ) , : )−pos mic ( micro gard ( j ) , : ) ) . ˆ 2 ) ) /c ) ) ;tdoa2ca l c {3∗2+raz }=[ tdoa2ca l c {3∗2+raz } ; ones ( l ength ( ordre temp ) ,1) ∗ [
i f i l e nof ] ones ( l ength ( ordre temp ) ,1) ∗ [ micro gard ( i ) micro gard (j ) ] ordre temp ’ t r ( 1 : nb td ) ’ ] ;
i f l ength ( cc )<nb td , break ; end ;pksmean=mean( cc ) ;[ pksmax , pksMax ind ] = max( cc ) ; pksMax ms=pksMax ind/indVms ;
pksMax ashydro=(pksMax ind−f en /nb sum) /( f e /nb sum) ;pksMid ind=round ( length ( cc ) /2) ; pksMid ms=pksMid ind/indVms ;
pksMid ashydro=(pksMid ind−f en /nb sum) /( f e /nb sum) ;pksmin = min( cc ) ;
i f plotCORiTDOA==1%[cc , l o c s ] = f indpeaks ( cc , ’MINPEAKDISTANCE’ , 1 , ’MINPEAKHEIGHT’ ,− Inf , ’
THRESHOLD’ , 0 , ’SORTSTR’ , ’ none ’ ) ;i s ubp l o t=i subp l o t +1; subplot ( Nij , Nraz∗(1+( hist0zoom1artzoom2no3 <3) ) ,
i s ubp l o t ) ; p l o t ( l i n s pa c e ( nofStart ms , nofEnd ms , l ength ( cc ) ) , cc ) ;x l ab e l ( [ num2str ( nofStart ms ) ’ < Temps ,ms < ’ num2str ( nofEnd ms ) ] , ’
FontSize ’ , f s i z e ) ;i f raz==1, strTDOA=[num2str ( pksMax ashydro−pksMid ashydro , ’%1.1 e ’ )
’ s ( ’ num2str ( [ ordre temp−pksMid ashydro ] , ’%1 .1 e ’ ) ] ;e l s e strTDOA=[num2str ( pksMax ashydro−pksMid ashydro , ’%1.1 e ’ )
15. APPENDIX4: MATLAB CODE FOR SIMULATION 115
’ s ’ num2str ( pksMax ind−pksMid ind ) ’ i ’ num2str (pksMax ms−pksMid ms , ’%1.1 e ’ ) ’ms ’ ] ; end ;
t i t l e ( [ CorrOrderStr{ raz} num2str ( [ i j ] ) ’−mics . TDOA=’ strTDOA ] , ’FontSize ’ , f s i z e ) ;
%t i t l e ( [ CorrOrderStr{ raz} num2str ( [ i j ] ) ’−mics . ’ ] , ’ FontSize ’ , f s i z e) ;
ax i s ( [ nofStart ms nofEnd ms pksmin 1.05∗pksmax ] ) ;%axes o f f ;i f hist0zoom1artzoom2no3<3i subp l o t=i subp l o t +1; subplot ( Nij , Nraz∗(1+( hist0zoom1artzoom2no3 <3) ) ,
i s ubp l o t ) ;end ;i f hist0zoom1artzoom2no3==0h i s t ( cc , l i n s pa c e ( pksmin , pksmax , 5 0 ) ) ;x l ab e l ( [ num2str ( pksmin , ’%3.1 e ’ ) ’ < CorrPeaks Amplitude < ’ num2str (
pksmax , ’%3.1 e ’ ) ] , ’ FontSize ’ , f s i z e ) ;ax i s ( [ pksmin pksmax 0 10 ] ) ;e l s e i f hist0zoom1artzoom2no3==1p lo t ( l i n s pa c e ( nofStart ms , nofEnd ms , l ength ( cc ) ) , cc ) ;ax i s ( [ nofStart ms nofEnd ms pksmean−(pksmax−pksmean ) pksmean+(pksmax
−pksmean ) ] ) ;zoom(4) ; zoom xon 5 ; zoom xon 5 ; zoom xon 5 ; zoom xon 5 ; zoom xon 5 ;
zoom xon 5 ;e l s e i f hist0zoom1artzoom2no3==2delMaxMid ind=abs ( pksMax ind−pksMid ind ) ;indzoom=pksMid ind−4∗delMaxMid ind : pksMid ind+4∗delMaxMid ind ;p l o t ( l i n s pa c e ( indzoom (1) / indINmsec−nofMid ms , indzoom ( end ) / indINmsec−
nofMid ms , l ength ( cc ( indzoom ) ) ) , cc ( indzoom ) ) ;x l ab e l ( [ num2str ( indzoom (1) / indINmsec−nofMid ms ) ’ < Temps ,ms < ’
num2str ( indzoom ( end ) / indINmsec−nofMid ms ) ] , ’ FontSize ’ , f s i z e ) ;end ;%axes o f f ;end ;
[ cc , l o c s ] = f indpeaks ( cc , ’MINPEAKDISTANCE’ , 1 , ’MINPEAKHEIGHT’ ,− Inf , ’THRESHOLD’ , 0 , ’SORTSTR’ , ’ none ’ ) ;
i f 3∗2+raz==C cortypeALLordre temp{ i , j }( f i nd ( nof==vecTimesTracking ) , 1 : nb td )=ordre temp
( 1 : nb td ) ;ALLtr{ i , j }( f i nd ( nof==vecTimesTracking ) , 1 : nb td )=t r ( 1 : nb td ) ;end ;end ;end ;
endendi f plotCORiTDOA==1
saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor )globEXTcourt{ i f i l e } ’ CORiTDOA t=’ num2str ( nof ) cortype{ i i } ’ . jpg ’ ] );
saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor )globEXTcourt{ i f i l e } ’ CORiTDOA t=’ num2str ( nof ) cortype{ i i } ’ . eps’ ] , ’ psc2 ’ ) ;
saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor )globEXTcourt{ i f i l e } ’ CORiTDOA t=’ num2str ( nof ) cortype{ i i } ’ . f i g ’ ] );
c l o s e a l l ;end ;
end ;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%tdoa=tdoa2ca l c {C cortype } ;i f isempty ( tdoa )==0
d e l a i =[ d e l a i ; tdoa ] ;end ;
end ; % f i n pour chaque f e n e t r e nof de 10 s%c l o s e (h) ; c l o s e a l l ;tr AVER{ i f i l e }=mean( tr ALL{ i f i l e } ,1) ;tr STD{ i f i l e }=std ( tr ALL{ i f i l e } , 1 ,1 ) ;%f i g u r e ;f o r imax=1: nb td
i f l ength ( tr HISTy{ i f i l e , imax})∗ l ength ( tr HISTx{ i f i l e , imax})==0tr HISTy{ i f i l e , imax}=0;tr HISTx{ i f i l e , imax}=0;
e l s e[ tr HISTy{ i f i l e , imax} , tr HISTx{ i f i l e , imax } ] = h i s t ( tr ALL{ i f i l e } ( : , imax ) ) ;
end ;end ;
end ; % f i n pour chaque f i c h i e r i f i l e
116 BIBLIOGRAPHY
de l a i 1=d e l a i ;save ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ’TDOA’
num2str ( [ C percTDOArefine ] ) ’ percSegm ’ num2str ( iSegmentTrajectory ) ’ . mat ’ ] , ’ tdoa ’ , ’de l a i ’ , ’ de la i1 ’ , ’ tr HISTx ’ , ’ tr HISTy ’ , ’ ALLtr ’ , ’ ALLordre temp ’ ) ;
end ;%−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−%load ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) num2str ( [ ovMYmsec s i zeTrack
C percTDOArefine ] ) ’ patTDOAinit mic=650K.mat ’ ] ) ;%save ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ’TDOA’
num2str ( [ C percTDOArefine ] ) ’ perc ’ ’ patTDOAinit mic=650K.mat ’ ] , ’ tdoa ’ , ’ de l a i ’ , ’ de la i1’ , ’ tr HISTx ’ , ’ tr HISTy ’ , ’ ALLtr ’ , ’ ALLordre temp ’ ) ;
load ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ’TDOA’num2str ( [ C percTDOArefine ] ) ’ percSegm ’ num2str ( iSegmentTrajectory ) ’ . mat ’ ] ) ;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% RECHERCHE DES QUADRUPLETS VERIFIANT LES SOMMES DES DELAIS%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%de l a i = [ ] ;p i k l 2 = [ ] ; p jh l 5 = [ ] ; p ih l 4 = [ ] ; do l j a = [ ] ; was3=4;ALLnof=startMYmsec :1∗ovMYmsec : nb secMYmsec ; Nnof=length (ALLnof ) ; min i f=i n f ∗ones (4 , Nnof ) ;
n o t r i p l e t=s i z e ( tdoa , 1 ) ; a l l i f = [ ] ; %prosto ctob by l i daze e s l i C re f i ne tdoa =[ ]%−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−i f sum( s i z e ( d e l a i 1 ) )==0, break ; end ;
i f C percTDOArefine>0f o r C re f i ne tdoa =[1]
d i sp ( [ da t e s t r (now) ’ C re f i ne tdoa C re f ine tdoa RECHERCHE DES QUADRUPLETS VERIFIANTLES SOMMES DES DELAIS ’ ] ) ;
f o r i f i l e=ichantPercentShow (0 , 1 , ’ r e f i n e . . . ’ , [ ] , f a l s e ) ; %h1 = waitbar ( 0 , [ ’ r e f i n e t doa f o r ’
num2str ( i f i l e ) ] ) ;i n o f =0; ALLnof=startMYmsec :1∗ovMYmsec : nb secMYmsec ; Nnof=length (ALLnof ) ;min i f=i n f ∗ones (4 , Nnof ) ; a l l i f = [ ] ;f o r nof=startMYmsec :1∗ovMYmsec : nb secMYmsec
tdoa=de l a i 1 ( f i nd ( d e l a i 1 ( : , 1 )==i f i l e & de l a i 1 ( : , 2 )==nof ) , 3 : was3+2) ;tdoa ( : , was3+1)=ze ro s ( s i z e ( tdoa , 1 ) ,1 ) ;micro gard=micro ( f i nd (sum ( ( [ tdoa ( : , 1 ) ; tdoa ( : , 2 ) ]∗ ones (1 , l ength ( micro ) ) )==(
ones (2∗ s i z e ( tdoa , 1 ) ,1 ) ∗micro ) ) ˜=0) ) ;i n o f=i no f +1;PercentShow ( ino f , l ength ( startMYmsec :1∗ovMYmsec : nb secMYmsec ) , [
da t e s t r ( c l o ck ) ’ r e f i n e ’ globEXTcourt{ i f i l e } ’ gard ’ num2str ( micro gard )] , [ ] , t rue ) ; %waitbar ( nof /( nb secMYmsec−startMYmsec ) ) ;
i f l ength ( micro gard )==4i f C percTDOArefine<=0
n o t r i p l e t=s i z e ( tdoa , 1 ) ;tdoa ( : , end+1)=(1: n o t r i p l e t ) ’ ;d o l j a =[ do l j a 1 ] ;
e l s en o t r i p l e t =1;i =1;j =2;k=3;h=4;l 1=f ind ( tdoa ( : , 1 )==micro gard ( i ) & tdoa ( : , 2 )==micro gard ( j ) ) ;l 2=f ind ( tdoa ( : , 1 )==micro gard ( i ) & tdoa ( : , 2 )==micro gard (k ) ) ;l 3=f ind ( tdoa ( : , 1 )==micro gard ( j ) & tdoa ( : , 2 )==micro gard (k ) ) ;l 4=f ind ( tdoa ( : , 1 )==micro gard ( i ) & tdoa ( : , 2 )==micro gard (h) ) ;l 5=f ind ( tdoa ( : , 1 )==micro gard ( j ) & tdoa ( : , 2 )==micro gard (h) ) ;l 6=f ind ( tdoa ( : , 1 )==micro gard (k ) & tdoa ( : , 2 )==micro gard (h) ) ;i f 1=abs ( tdoa ( l1 , 3 )+tdoa ( l3 , 3 )−tdoa ( l2 , 3 ) ) ; min i f (1 , i n o f )=quant i l e (
i f 1 , C percTDOArefine /100) ;i f 2=abs ( tdoa ( l3 , 3 )+tdoa ( l6 , 3 )−tdoa ( l5 , 3 ) ) ; min i f (2 , i n o f )=quant i l e (
i f 2 , C percTDOArefine /100) ;i f 3=abs ( tdoa ( l2 , 3 )+tdoa ( l6 , 3 )−tdoa ( l4 , 3 ) ) ; min i f (3 , i n o f )=quant i l e (
i f 3 , C percTDOArefine /100) ;i f 4=abs ( tdoa ( l1 , 3 )+tdoa ( l5 , 3 )−tdoa ( l4 , 3 ) ) ; min i f (4 , i n o f )=quant i l e (
i f 4 , C percTDOArefine /100) ;a l l i f =[ a l l i f i f 1 i f 2 i f 3 i f 4 ] ;
f o r p i j=l1 ’f o r pjk=l3 ’f o r pik=l2 ’p i k l 2 =[ pik abs ( tdoa ( p i j , 3 )+tdoa ( pjk , 3 )−tdoa ( pik , 3 ) ) ] ;abs1=abs ( tdoa ( p i j , 3 )+tdoa ( pjk , 3 )−tdoa ( pik , 3 ) ) ; sq1=abs1∗abs1 ;i f abs1<=mini f (1 , i n o f )f o r pkh=l6 ’
15. APPENDIX4: MATLAB CODE FOR SIMULATION 117
f o r pjh=l5 ’p jh l5 =[ p jh l5 abs ( tdoa ( pjk , 3 )+tdoa (pkh , 3 )−tdoa ( pjh , 3 ) ) ] ;abs2=abs ( tdoa ( pjk , 3 )+tdoa (pkh , 3 )−tdoa ( pjh , 3 ) ) ; sq2=abs2∗abs2 ;i f abs2<=mini f (2 , i n o f )f o r pih=l4 ’p ih l 4 =[ p ih l 4 abs ( tdoa ( pik , 3 )+tdoa (pkh , 3 )−tdoa ( pih , 3 ) ) abs ( tdoa ( p i j
, 3 )+tdoa ( pjh , 3 )−tdoa ( pih , 3 ) ) ] ;abs3=abs ( tdoa ( pik , 3 )+tdoa (pkh , 3 )−tdoa ( pih , 3 ) ) ; sq3=abs3∗abs3 ;abs4=abs ( tdoa ( p i j , 3 )+tdoa ( pjh , 3 )−tdoa ( pih , 3 ) ) ; sq4=abs4∗abs4 ;i f abs3<= mini f (3 , i n o f ) && abs4<=mini f (4 , i n o f )
t o t a lE r r o r ( ichant , iSegmentTrajectory , indC DivsMaxsPers )=to t a lE r r o r (ichant , iSegmentTrajectory , indC DivsMaxsPers )+sq1+sq2+sq3+sq4 ;
i f tota lErrorMin ( ichant , iSegmentTrajectory , indC DivsMaxsPers )>sq1+sq2+sq3+sq4 , tota lErrorMin ( ichant , iSegmentTrajectory ,indC DivsMaxsPers )=sq1+sq2+sq3+sq4 ; end ;
i f totalErrorMax ( ichant , iSegmentTrajectory , indC DivsMaxsPers )<sq1+sq2+sq3+sq4 , totalErrorMax ( ichant , iSegmentTrajectory ,indC DivsMaxsPers )=sq1+sq2+sq3+sq4 ; end ;
i f ( tdoa ( pik , end ) ˜=0) , tdoa ( pik , end+1)=n o t r i p l e t ;e l s e tdoa ( pik , was3+min( f i nd ( tdoa ( pik , was3+1:end )==0)) )=n o t r i p l e t ;
end ;i f ( tdoa ( p i j , end ) ˜=0) , tdoa ( p i j , end+1)=n o t r i p l e t ;e l s e tdoa ( p i j , was3+min( f i nd ( tdoa ( p i j , was3+1:end )==0)) )=n o t r i p l e t ;
end ;i f ( tdoa ( pjk , end ) ˜=0) , tdoa ( pjk , end+1)=n o t r i p l e t ;e l s e tdoa ( pjk , was3+min( f i nd ( tdoa ( pjk , was3+1:end )==0)) )=n o t r i p l e t ;
end ;i f ( tdoa ( pjh , end ) ˜=0) , tdoa ( pjh , end+1)=n o t r i p l e t ;e l s e tdoa ( pjh , was3+min( f i nd ( tdoa ( pjh , was3+1:end )==0)) )=n o t r i p l e t ;
end ;i f ( tdoa (pkh , end ) ˜=0) , tdoa (pkh , end+1)=n o t r i p l e t ;e l s e tdoa (pkh , was3+min( f i nd ( tdoa (pkh , was3+1:end )==0)) )=n o t r i p l e t ;
end ;i f ( tdoa ( pih , end ) ˜=0) , tdoa ( pih , end+1)=n o t r i p l e t ;e l s e tdoa ( pih , was3+min( f i nd ( tdoa ( pih , was3+1:end )==0)) )=n o t r i p l e t ;
end ;n o t r i p l e t=n o t r i p l e t +1;endendendendendendendendendNt r i p l e t s ( ichant , iSegmentTrajectory , indC DivsMaxsPers )=n o t r i p l e t ;do l j a =[ do l j a l ength ( l 1 )∗ l ength ( l 2 )∗ l ength ( l 3 )∗ l ength ( l 4 )∗ l ength ( l 5 )∗
l ength ( l 6 ) / n o t r i p l e t ] ;endt d e l a i 2=s i z e ( tdoa , 2 ) ;i f rou==0
i f ( tde l a i 2<t d e l a i ) , tdoa (1 , t d e l a i )=0; endi f ( tde l a i 2>t d e l a i ) , d e l a i (1 , t d e l a i 2 +2)=0; end
endrou=0;d e l a i =[ d e l a i ; ones ( s i z e ( tdoa , 1 ) ,1 ) ∗ [ i f i l e nof ] tdoa ] ;t d e l a i=s i z e ( de l a i , 2 ) −2;
end%save ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) ’TDOA’ num2str
( [ C percTDOArefine ] ) ’ perc ’ ’ patTDOAafter mic=650K.mat ’ ] , ’ tdoa ’ , ’ de la i1’ , ’ d e l a i ’ ) ;
end ; % f i n pour chaque f e n e t r e nof de 10 s%c l o s e ( h1 ) ; c l o s e a l l ;
end ; % f i n pour chaque f i c h i e r i f i l esave ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ’
TDOA’ num2str ( [ C percTDOArefine ] ) ’ percSegm ’ num2str ( iSegmentTrajectory ) ’ . mat’ ] , ’ tdoa ’ , ’ de la i1 ’ , ’ d e l a i ’ , ’ do l ja ’ , ’ mini f ’ , ’ n o t r i p l e t ’ , ’ a l l i f ’ , ’ t o ta lEr ro r ’ , ’
end ;%−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−%load ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) num2str ( [ ovMYmsec s i zeTrack
C percTDOArefine ] ) ’ patTDOAafter mic=650K.mat ’ ] ) ;%save ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ’TDOA’
num2str ( [ C percTDOArefine ] ) ’ perc ’ ’ patTDOAafter mic=650K.mat ’ ] , ’ tdoa ’ , ’ de la i1 ’ , ’ d e l a i ’ );
i f C percTDOArefine>0load ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ’TDOA’
num2str ( [ C percTDOArefine ] ) ’ percSegm ’ num2str ( iSegmentTrajectory ) ’ . mat ’ ] ) ;end ;i f e x i s t ( ’ ALLnof ’ )
f i g u r e ;p l o t (ALLnof ,mean( minif , 1 ) /1000) ;x l ab e l ( [ ’ Temps ,ms ’ ] , ’ FontSize ’ , f s i z e ) ;y l ab e l ( [ ’ Threshold ,ms ’ ] , ’ FontSize ’ , f s i z e ) ;t i t l e ( [ globEXTcourt{ i chant } ’ percentage o f TDOA=’ num2str ( [ C percTDOArefine ] ) ’
Thresholds ’ ] ) ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ’
c l o s e ( gc f ) ;f i g u r e ;h i s t ( a l l i f /1000) ;x l ab e l ( [ ’ Threshold ,ms ’ ] , ’ FontSize ’ , f s i z e ) ;%y l abe l ( [ ’ Threshold ,ms ’ ] , ’ FontSize ’ , f s i z e ) ;t i t l e ( [ globEXTcourt{ i chant } ’ percentage o f TDOA=’ num2str ( [ C percTDOArefine ] ) ’
Histogram of Thresh ’ ] ) ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ’
c l o s e ( gc f ) ;end ;micro gard=micro ; %8MAY%−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−i f s i z e ( de l a i , 2 ) <7, l i n e sESTt r i p l=ones ( s i z e ( d e l a i 1 ( : , 1 ) ) ) ; e l s e l i n e sESTt r i p l=( d e l a i ( : , 7 )
>=1) ; end ;i nd l i n e sESTt r i p l=f i nd ( l i n e sESTt r i p l ) ;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%plotTDOAfromdelai1=1;%plotTDOAfromdelai1=s i z e ( de l a i , 2 )<7;%WAS BEFORE MARS20plotTDOA1fromdelai1=0;%WAS BEFORE MARS20f o r i f i l e=ichant
f o r i nd i =1: l ength ( micro gard )i=micro gard ( i nd i ) ;f o r i nd j=i : l ength ( micro gard )
j=micro gard ( i nd j ) ;i f i==j , cont inue ; end ; % i f i>=j , cont inue ; end ;plotTIME{ i f i l e , i , j}=− i n f ∗ones (1 , l ength ( vecTimesTracking ) ) ;plotTDOA1{ i f i l e , i , j}=− i n f ∗ones ( l ength ( vecTimesTracking ) , nb td ) ;plotTDOA{C INDpercTDOArefine , i f i l e , i , j}=− i n f ∗ones ( l ength ( vecTimesTracking ) , nb td
) ;plotHmax1{ i f i l e , i , j}=− i n f ∗ones ( l ength ( vecTimesTracking ) , nb td ) ;plotHmax{C INDpercTDOArefine , i f i l e , i , j}=− i n f ∗ones ( l ength ( vecTimesTracking ) , nb td
) ;f o r i n o f =1: l ength ( vecTimesTracking ) %=ind l i n e sESTt r i p l
plotTIME{ i f i l e , i , j }( i n o f )=vecTimesTracking ( i n o f ) ; %vecTimesTracking (l i n e sESTt r i p l ( i n o f ) ) ;
i f TDOA0FD123type<=0linesOurTime1=(de l a i 1 ( : , 1 )==i f i l e )&( d e l a i 1 ( : , 2 )==vecTimesTracking ( i n o f ) )
&( d e l a i 1 ( : , 3 )==i )&( d e l a i 1 ( : , 4 )==j ) ;i f plotTDOAfromdelai1
indl inesOurTime=f ind ( linesOurTime1 ) ;f o r i i =1: l ength ( indl inesOurTime )
15. APPENDIX4: MATLAB CODE FOR SIMULATION 119
i f i s i n f ( d e l a i 1 ( indl inesOurTime ( i i ) ,5 ) )s d f s d f s d f =12;end ;
plotTDOA{C INDpercTDOArefine , i f i l e , i , j }( ino f , i i )=de l a i 1 (indl inesOurTime ( i i ) ,5 ) ;%/ovMYmsec ; %APR30 PODGON TDOA
plotHmax{C INDpercTDOArefine , i f i l e , i , j }( ino f , i i )=de l a i 1 (indl inesOurTime ( i i ) ,6 ) ;
end ;e l s e
l inesOurTime=( d e l a i ( : , 1 )==i f i l e )&( d e l a i ( : , 2 )==vecTimesTracking ( i n o f ))&( d e l a i ( : , 3 )==i )&( d e l a i ( : , 4 )==j )&l i n e sESTt r i p l ;
indl inesOurTime=f ind ( linesOurTime ) ;i f TDOA0FD123type<=0f o r i i =1: l ength ( indl inesOurTime )plotTDOA{C INDpercTDOArefine , i f i l e , i , j }( ino f , i i )=d e l a i (
indl inesOurTime ( i i ) ,5 ) ;%/ovMYmsec ; %APR30 PODGON TDOAplotHmax{C INDpercTDOArefine , i f i l e , i , j }( ino f , i i )=d e l a i (
indl inesOurTime ( i i ) ,6 ) ;end ;end ;
end ;i f plotTDOA1fromdelai1 %i f s i z e ( de l a i , 2 )>=6
plotTDOA1{ i f i l e , i , j }( ino f , : )=de l a i 1 ( linesOurTime1 , 5 ) ;%/ovMYmsec ; %APR30 PODGON TDOA
plotHmax1{ i f i l e , i , j }( ino f , : )=de l a i 1 ( linesOurTime1 , 6 ) ;end ;
e l s eplotTDOA{C INDpercTDOArefine , i f i l e , i , j }( ino f , : )=dfmaxs{TDOA0FD123type , i ,
j }( ino f , : ) ;end ;
end ;%plotTDOA{C INDpercTDOArefine , i f i l e , i , j } ( : , : )
plotTIME{ i f i l e , j , i}=plotTIME{ i f i l e , i , j } ;plotTDOA1{ i f i l e , j , i}=−plotTDOA1{ i f i l e , i , j } ;plotTDOA{C INDpercTDOArefine , i f i l e , j , i}=−plotTDOA{C INDpercTDOArefine , i f i l e , i , j
} ;plotHmax1{ i f i l e , j , i}=plotHmax1{ i f i l e , i , j } ;plotHmax{C INDpercTDOArefine , i f i l e , j , i}=plotHmax{C INDpercTDOArefine , i f i l e , i , j } ;
end ;end ;
end ;
f o r C calcIplotTDOAiHmax=[1]%LineSty l eS t r ={ ’ − ’ , ’ − − ’ , ’ − . ’ , ’ : ’ , ’ : ’} ;%MarkerStr={ ’o ’ , ’ d ’ , ’+ ’ , ’ x ’ , ’ . ’ } ;Nsubp lot l ine =4;xTDOAbefIaft=c e l l ( nb f i c h i e r , l ength ( micro gard ) , l ength ( micro gard ) ) ; %{ i f i l e , imic , jmic}=
plotTIME{ i f i l e , imic , jmic } ;yTDOAbefore=c e l l ( nb f i c h i e r , l ength ( micro gard ) , l ength ( micro gard ) ) ; %plotTDOA1{ i f i l e ,
imic , jmic } ( : , 1 : 4 ) ;yTDOAafter=c e l l ( nb f i c h i e r , l ength ( micro gard ) , l ength ( micro gard ) ) ; %plotTDOA{
C INDpercTDOArefine , i f i l e , imic , jmic } ( : , 1 : 4 ) ;f o r i f i l e=ichant
f o r imic=micro gardf o r jmic=micro gard
i f imic>=jmic , cont inue ; end ;t i t l e s t r ={ ’combTDOA before ’ , ’combTDOA af t e r ’ , ’ combHeight before ’ , ’ combHeight
a f t e r ’ , ’ sigTDOA ’ , ’ s igHeight ’ } ;u n i t s t r ={ ’ ms ’ , ’ ms ’ , ’ ’ , ’ ’ , ’ ms ’ , ’ Hz ’} ;range=[maxTDOAms, maxTDOAms, maxAmpl , maxAmpl , maxTDOAms, maxAmpl ] ;vecSubplots ={[1 2 ] , [ 3 4 ] } ;i f plotCORRS==1
f o r imegaplot=1: s i z e ( vecSubplots , 2 )s c r s z=get (0 , ’ ScreenSize ’ ) ;f i g u r e ( ’ Pos i t ion ’ , [ 0 0 s c r s z (3 ) s c r s z (4 ) ] ) ;i s ubp l o t =0;f o r i s u b p l o t l i n e=vecSubplots{ imegaplot}i f i s ubp l o t l i n e <=2, x=l i n spa c e ( vecTimesTracking (1) ,
vecTimesTracking ( end ) , s i z e (ALLtr{ imic , jmic } ,1) ) ;e l s e i f i s ubp l o t l i n e >=3, x=plotTIME{ i f i l e , imic , jmic } ;end ;i f i s u b p l o t l i n e==1, y1to5=plotTDOA1{ i f i l e , imic , jmic } ( : , 1 : nb td ) ;e l s e i f i s u b p l o t l i n e==2, y1to5=plotTDOA{C INDpercTDOArefine , i f i l e ,
imic , jmic } ( : , 1 : nb td ) ;
120 BIBLIOGRAPHY
e l s e i f i s u b p l o t l i n e==3, y1to5=plotHmax1{ i f i l e , imic , jmic } ( : , 1 : nb td ) ;e l s e i f i s u b p l o t l i n e==4, y1to5=plotHmax{C INDpercTDOArefine , i f i l e ,
imic , jmic } ( : , 1 : nb td ) ;e l s e i f i s u b p l o t l i n e==5, y1to5=ALLordre temp{ imic , jmic } ( : , 1 : nb td ) ;e l s e i f i s u b p l o t l i n e==6, y1to5=ALLtr{ imic , jmic } ( : , 1 : nb td ) ;
end ;i s ubp l o t=i subp l o t +1; subplot ( l ength ( vecSubplots{ imegaplot }) ,3 ,
i s ubp l o t ) ; hold on ;f o r i p l o t =1: nb tdp lo t (x , y1to5 ( : , i p l o t ) , ’ LineWidth ’ , 2 , ’ L ineSty le ’ , ’ − ’ , ’ MarkerSize ’ ,
msize , ’ Marker ’ , ’ o ’ ) ;end ;ylim ([− range ( i s u b p l o t l i n e ) range ( i s u b p l o t l i n e ) ] ) ; xlim ( [ x (1) x ( end )
f s i zeSm ) ;i f l abe l 0 range1 s imp l e==0x l abe l ( [ num2str (0 .01∗ round (100∗x (1) ) ) ’ < Temps ,ms < ’ num2str
(0 .01∗ round (100∗x ( end ) ) ) ] , ’ FontSize ’ , f s i z e ) ;y l ab e l ( [ num2str (0 .01∗ round (100∗min(min ( range ( i s u b p l o t l i n e ) ) ) ) ) ’< ’
u n i t s t r { i s u b p l o t l i n e } ’< ’ num2str (0 .01∗ round (100∗max(max( range (i s u b p l o t l i n e ) ) ) ) ) ] , ’ FontSize ’ , f s i z e ) ;
e l s ex l ab e l ( [ ’ Temps ,ms ’ ] , ’ FontSize ’ , f s i z e ) ;y l ab e l ( [ t i t l e s t r { i s u b p l o t l i n e } un i t s t r { i s u b p l o t l i n e } ] , ’ FontSize ’ ,
f s i z e ) ;end ;t i t l e ( [ t i t l e s t r { i s u b p l o t l i n e } ’ a l l maxs ’ ] , ’ FontSize ’ , f s i z e ) ;f o r imax=1:min (2 ,Nmax)i subp l o t=i subp l o t +1; subplot ( l ength ( vecSubplots{ imegaplot }) ,3 ,
i s ubp l o t ) ;p l o t (x , y1to5 ( : , imax ) , ’ LineWidth ’ , 2 , ’ L ineSty le ’ , L ineSty l eS t r { imax } , ’
MarkerSize ’ , msize , ’ Marker ’ , MarkerStr{ imax}) ;ylim ([− range ( i s u b p l o t l i n e ) range ( i s u b p l o t l i n e ) ] ) ; xlim ( [ x (1) x ( end )
] ) ;%legend ( [ ’max ’ num2str ( imax ) ] , ’ Location ’ , ’ Best ’ , ’ FontSize ’ , f s i zeSm ) ;i f l abe l 0 range1 s imp l e==0x l abe l ( [ num2str (0 .01∗ round (100∗x (1) ) ) ’ < Temps ,ms < ’ num2str
(0 .01∗ round (100∗x ( end ) ) ) ] , ’ FontSize ’ , f s i z e ) ;y l ab e l ( [ num2str (0 .01∗ round (100∗min(min ( range ( i s u b p l o t l i n e ) ) ) ) ) ’< ’
u n i t s t r { i s u b p l o t l i n e } ’< ’ num2str (0 .01∗ round (100∗max(max( range (i s u b p l o t l i n e ) ) ) ) ) ] , ’ FontSize ’ , f s i z e ) ;
e l s ex l ab e l ( [ ’ Temps ,ms ’ ] , ’ FontSize ’ , f s i z e ) ;y l ab e l ( [ t i t l e s t r { i s u b p l o t l i n e } un i t s t r { i s u b p l o t l i n e } ] , ’ FontSize ’ ,
f s i z e ) ;end ;t i t l e ( [ t i t l e s t r { i s u b p l o t l i n e } ’ max ’ num2str ( imax ) ] , ’ FontSize ’ , f s i z e
) ;end ;end ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor )
globEXTcourt{ i chant } ’TDOAiHmax’ num2str ( [ C percTDOArefine ] ) ’perc ’ ’Mics=’ num2str ( [ imic jmic ] ) ’ ’ t i t l e s t r {vecSubplots{imegaplot }(1) } t i t l e s t r {vecSubplots{ imegaplot }( end )} ’ iSegm ’num2str ( iSegmentTrajectory ) ’ . jpg ’ ] ) ;
%saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor )globEXTcourt{ i chant } ’TDOAiHmax’ num2str ( [ C percTDOArefine ] ) ’perc ’ ’Mics=’ num2str ( [ imic jmic ] ) ’ ’ t i t l e s t r {vecSubplots{imegaplot }(1) } t i t l e s t r {vecSubplots{ imegaplot }( end )} ’ iSegm ’num2str ( iSegmentTrajectory ) ’ . f i g ’ ] ) ;
saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor )globEXTcourt{ i chant } ’TDOAiHmax’ num2str ( [ C percTDOArefine ] ) ’perc ’ ’Mics=’ num2str ( [ imic jmic ] ) ’ ’ t i t l e s t r {vecSubplots{imegaplot }(1) } t i t l e s t r {vecSubplots{ imegaplot }( end )} ’ iSegm ’num2str ( iSegmentTrajectory ) ’ . eps ’ ] , ’ psc2 ’ ) ;
saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor )globEXTcourt{ i chant } ’TDOAiHmax’ num2str ( [ C percTDOArefine ] ) ’perc ’ ’Mics=’ num2str ( [ imic jmic ] ) ’ ’ t i t l e s t r {vecSubplots{imegaplot }(1) } t i t l e s t r {vecSubplots{ imegaplot }( end )} ’ iSegm ’num2str ( iSegmentTrajectory ) ’ . t i f ’ ] ) ;
c l o s e a l l ;end ;
end ;xTDOAbefIaft{ i f i l e , imic , jmic}=plotTIME{ i f i l e , imic , jmic } ;yTDOAbefore{ i f i l e , imic , jmic}=plotTDOA1{ i f i l e , imic , jmic } ( : , 1 : nb td ) ;
15. APPENDIX4: MATLAB CODE FOR SIMULATION 121
yTDOAafter{ i f i l e , imic , jmic}=plotTDOA{C INDpercTDOArefine , i f i l e , imic , jmic} ( : , 1 : nb td ) ;
yTDOAbefore{ i f i l e , jmic , imic}=−plotTDOA1{ i f i l e , imic , jmic } ( : , 1 : nb td ) ;yTDOAafter{ i f i l e , jmic , imic}=−plotTDOA{C INDpercTDOArefine , i f i l e , imic , jmic
} ( : , 1 : nb td ) ;end
endendsave ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ’TDOA’
f o r C subplots1onep lot2 =[ ] %[2 1 ]f o r C plotBEFiAFT=[1 nb td ]
s c r s z=get (0 , ’ ScreenSize ’ ) ;f i g u r e ( ’ Pos i t ion ’ , [ 0 0 s c r s z (3 ) s c r s z (4 ) ] ) ;range=[maxAmpl , maxTDOAms, maxTDOAms, maxTDOAms, maxAmpl , maxAmpl ] ;i s u b p l o t l i n e =0;f o r i f i l e=ichant
f o r imic=micro gardf o r jmic=micro gard
i f imic>=jmic , cont inue ; end ;i f C subplots1onep lot2==1
i s u b p l o t l i n e=i s u b p l o t l i n e +1; subplot (3 ,4 , i s u b p l o t l i n e ) ;e l s e
subplot (1 , 2 , 1 ) ;end ;hold on ;f o r imax=1:C plotBEFiAFT
plo t ( xTDOAbefIaft{ i f i l e , imic , jmic } , yTDOAbefore{ i f i l e , imic , jmic } ( : ,imax ) , ’ LineWidth ’ , 1 , ’ L ineSty le ’ , L ineSty l eS t r { imax } , ’ MarkerSize ’ ,msize /2 , ’Marker ’ , MarkerStr{ imax}) ;
end ;i f C subplots1onep lot2==1
t i t l e ( [ ’ a l l Ts , mics=’ num2str ( [ imic jmic ] ) ] ) ;ylim ([−maxTDOAms maxTDOAms] ) ;
e l s et i t l e ( [ ’ a l l Ts ’ ] ) ;ylim ([−maxTDOAms maxTDOAms] ) ; %ylim ( [ 0 . 0 7 5 0 . 1 3 5 ] ) ;
end ;
xlim ( [ xTDOAbefIaft{ i f i l e , imic , jmic }(1) xTDOAbefIaft{ i f i l e , imic , jmic }( end) ] ) ;
x l ab e l ( [ ’ time moments (Ts ) ,ms ’ ] , ’ FontSize ’ , f s i z e ) ;y l ab e l ( [ ’TDOA,ms ’ ] , ’ FontSize ’ , f s i z e ) ;i f C subplots1onep lot2==1
i s u b p l o t l i n e=i s u b p l o t l i n e +1; subplot (3 ,4 , i s u b p l o t l i n e ) ;e l s e
subplot (1 , 2 , 2 ) ;end ;hold on ;f o r imax=1:C plotBEFiAFT
plo t ( xTDOAbefIaft{ i f i l e , imic , jmic } , yTDOAafter{ i f i l e , imic , jmic } ( : ,imax ) , ’ LineWidth ’ , 1 , ’ L ineSty le ’ , L ineSty l eS t r { imax } , ’ MarkerSize ’ ,msize /2 , ’Marker ’ , MarkerStr{ imax}) ;
end ;i f C subplots1onep lot2==0
t i t l e ( [ ’ coherent Ts , mics=’ num2str ( [ imic jmic ] ) ] ) ;ylim ([−maxTDOAms maxTDOAms] ) ;
e l s et i t l e ( [ ’ coherent Ts ’ ] ) ;ylim ([−maxTDOAms maxTDOAms] ) ; %ylim ( [ 0 . 0 7 5 0 . 1 3 5 ] ) ;
end ;
xlim ( [ xTDOAbefIaft{ i f i l e , imic , jmic }(1) xTDOAbefIaft{ i f i l e , imic , jmic }( end) ] ) ;
x l ab e l ( [ ’ time moments (Ts ) ,ms ’ ] , ’ FontSize ’ , f s i z e ) ;y l ab e l ( [ ’TDOA,ms ’ ] , ’ FontSize ’ , f s i z e ) ;
endend
122 BIBLIOGRAPHY
saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{i chant } ’BEFiAFT type , perc , number ’ num2str ( [ C subplots1onep lot2C percTDOArefine C plotBEFiAFT ] ) ’Mics=’ num2str ( [ imic jmic ] ) ’ . jpg ’ ] ) ;
saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{i chant } ’BEFiAFT type , perc , number ’ num2str ( [ C subplots1onep lot2C percTDOArefine C plotBEFiAFT ] ) ’Mics=’ num2str ( [ imic jmic ] ) ’ . f i g ’ ] ) ;
saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{i chant } ’BEFiAFT type , perc , number ’ num2str ( [ C subplots1onep lot2C percTDOArefine C plotBEFiAFT ] ) ’Mics=’ num2str ( [ imic jmic ] ) ’ . eps ’ ] , ’ psc2’ ) ;
c l o s e a l l ;end
end ;end ;%−−−−−−−−−−−−−−−−−PLOT ENTROPY fo r va lues in vecALL percTDOArefine −−−−−−−−−−−−−−−−−−f o r C entropy =[ ]
maxtypesstr=c e l l (1 , nb td+3) ;f o r imax=1: nb td
maxtypesstr{ imax}=[num2str ( imax ) ’max ’ ] ;end ;maxtypesstr{nb td+1}= ’ a l l maxs ’ ;maxtypesstr{nb td+2}=’ a l l cohers ’ ;maxtypesstr{nb td+3}=’maxsXtripl ’ ;
f o r C delaiM6S6 =[ ]delaiM6 = [ ] ;d e l a iS6 = [ ] ;PercentShow (0 , 1 , ’ Compute delaiM6S6 . . . ’ , [ ] , f a l s e ) ;f o r i d e l a i =1: s i z e ( de l a i , 1 )
n t r i p l e t=length ( f i nd ( d e l a i ( i d e l a i , 7 : end )>0) ) ;PercentShow ( i d e l a i , s i z e ( de l a i , 1 ) , [ da t e s t r ( c l o ck ) ’ Compute delaiM6S6 f o r ’
globEXTcourt{ i f i l e } ] , [ ] , t rue ) ;i f n t r i p l e t >=1
de la iS6=[ de l a iS6 d e l a i ( i d e l a i , 7 ) ] ;end ;i f C percTDOArefine<100
f o r i t r i p l e t =1: n t r i p l e t%ide la iM=ide la iM+1;%delaiM=[delaiM ; d e l a i ( i d e l a i , : ) ] ;delaiM6=[delaiM6 n t r i p l e t ∗ones (1 , d e l a i ( i d e l a i , 7 ) ) ] ;
end ;end ;
end ;i f C percTDOArefine==100
delaiM6=de la iS6 ;end ;save ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ’
TDOA’ num2str ( [ C percTDOArefine ] ) ’ delaiM6S6 .mat ’ ] , ’ delaiM6 ’ , ’ de la iS6 ’ , ’ n t r i p l e t’ ) ;
end ;load ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ’TDOA’
s c r s z=get (0 , ’ ScreenSize ’ ) ;f i g u r e ( ’ Pos i t ion ’ , [ 0 0 s c r s z (3 ) s c r s z (4 ) ] ) ;f o r i f i l e=ichant
f o r imax=1: nb tdsubplot (2 , nb td , imax ) ; entr ( i f i l e , C percTDOArefine , imax )=entropy (
tr HISTy{ i f i l e , imax}) ;bar ( tr HISTx{ i f i l e , imax} , tr HISTy{ i f i l e , imax}) ;x l ab e l ( [ num2str (0 .01∗ round (100∗min( tr HISTx{ i f i l e , imax}) ) ) ’<ms<’ num2str (0 .01∗
round (100∗max( tr HISTx{ i f i l e , imax}) ) ) ] , ’ FontSize ’ , f s i z e ) ;y l ab e l ( [ ’ Number o f TDOAs,max ’ num2str ( imax ) ’< ’ num2str (0 .01∗ round (100∗max(
tr HISTy{ i f i l e , imax}) ) ) ] , ’ FontSize ’ , f s i z e ) ;t i t l e ( [ maxtypesstr{ imax} num2str ( C percTDOArefine ) ’% ’ ’ e=’ num2str (0 .01∗ round
(100∗ entr ( i f i l e , C percTDOArefine , imax ) ) ) ] , ’ FontSize ’ , f s i z e ) ;end
end
subplot (2 , nb td , nb td+[1 2 ] ) ;[ maxsBefore HISTy{ i f i l e , imax} , maxsBefore HISTx{ i f i l e , imax}]= h i s t ( d e l a i 1 ( : , 6 ) ,min ( d e l a i 1
( : , 6 ) ) : 0 . 3 : max( d e l a i 1 ( : , 6 ) ) ) ;i f l ength (maxsBefore HISTy{ i f i l e , imax})∗ l ength (maxsBefore HISTx{ i f i l e , imax})==0
maxsBefore HISTy{ i f i l e , imax}=0;maxsBefore HISTx{ i f i l e , imax}=0;
end ;
15. APPENDIX4: MATLAB CODE FOR SIMULATION 123
bar (maxsBefore HISTx{ i f i l e , imax} , maxsBefore HISTy{ i f i l e , imax}) ; entr ( i f i l e ,C percTDOArefine , nb td+1)=entropy (maxsBefore HISTy{ i f i l e , imax}) ;
x l ab e l ( [ num2str (0 .01∗ round (100∗min(maxsBefore HISTx{ i f i l e , imax}) ) ) ’ <TDOA,ms< ’ num2str(0 .01∗ round (100∗max(maxsBefore HISTx{ i f i l e , imax}) ) ) ] , ’ FontSize ’ , f s i z e ) ;
y l ab e l ( [ ’ Number o f a l l maxs TDOAs< ’ num2str (0 .01∗ round (100∗max(maxsBefore HISTy{ i f i l e ,imax}) ) ) ] , ’ FontSize ’ , f s i z e ) ;
xlim ( [ min (maxsBefore HISTx{ i f i l e , imax}) (max(maxsBefore HISTx{ i f i l e , imax})+min (maxsBefore HISTx{ i f i l e , imax})+1e−9) /2 ] ) ;
ylim ( [ min (maxsBefore HISTy{ i f i l e , imax}) (max(maxsBefore HISTy{ i f i l e , imax})+min (maxsBefore HISTy{ i f i l e , imax})+1e−9) /2 ] ) ;
t i t l e ( [ maxtypesstr{nb td+1} num2str ( C percTDOArefine ) ’% ’ ’ e=’ num2str (0 .01∗ round (100∗entr ( i f i l e , C percTDOArefine , nb td+1) ) ) ] , ’ FontSize ’ , f s i z e ) ;
i f ˜ isempty ( de l a iS6 )%subplot (2 , nb td , ( ( n b f i c h i e r +1)∗nb td−2) : ( ( n b f i c h i e r +1)∗nb td−1) ) ;subplot (2 , nb td , nb td+[3 4 ] ) ;[ maxsAfter HISTy{ i f i l e , imax} , maxsAfter HISTx{ i f i l e , imax}]= h i s t ( de la iS6 , min ( de l a iS6 )
: 0 . 3 : max( de l a iS6 ) ) ;i f l ength (maxsAfter HISTy{ i f i l e , imax})∗ l ength (maxsAfter HISTx{ i f i l e , imax})==0
maxsAfter HISTy{ i f i l e , imax}=0;maxsAfter HISTx{ i f i l e , imax}=0;
end ;bar (maxsAfter HISTx{ i f i l e , imax} , maxsAfter HISTy{ i f i l e , imax}) ; entr ( i f i l e ,
C percTDOArefine , nb td+2)=entropy (maxsAfter HISTy{ i f i l e , imax}) ;x l ab e l ( [ num2str (0 .01∗ round (100∗min(maxsAfter HISTx{ i f i l e , imax}) ) ) ’ <TDOA,ms< ’
num2str (0 .01∗ round (100∗max(maxsAfter HISTx{ i f i l e , imax}) ) ) ] , ’ FontSize ’ , f s i z e ) ;y l ab e l ( [ ’ Number o f a l l maxs TDOAs< ’ num2str (0 .01∗ round (100∗max(maxsAfter HISTy{
i f i l e , imax}) ) ) ] , ’ FontSize ’ , f s i z e ) ;xlim ( [ min (maxsAfter HISTx{ i f i l e , imax}) (max(maxsAfter HISTx{ i f i l e , imax})+min (
maxsAfter HISTx{ i f i l e , imax})+1e−9) /2 ] ) ;ylim ( [ min (maxsAfter HISTy{ i f i l e , imax}) (max(maxsAfter HISTy{ i f i l e , imax})+min (
maxsAfter HISTy{ i f i l e , imax})+1e−9) /2 ] ) ;t i t l e ( [ maxtypesstr{nb td+2} num2str ( C percTDOArefine ) ’% ’ ’ e=’ num2str (0 .01∗ round
(100∗ entr ( i f i l e , C percTDOArefine , nb td+2) ) ) ] , ’ FontSize ’ , f s i z e ) ;end ;
i f ˜ isempty ( delaiM6 )%subplot (2 , nb td , ( n b f i c h i e r +1)∗nb td ) ;subplot (2 , nb td , nb td+nb td ) ;h i s t ( delaiM6 , min ( delaiM6 ) : 0 . 3 : max( delaiM6 ) ) ;[ maxsXtriplet HISTy{ i f i l e , imax} , maxsXtriplet HISTx{ i f i l e , imax}]= h i s t ( delaiM6 , min (
delaiM6 ) : 0 . 3 : max( delaiM6 ) ) ;i f l ength ( maxsXtriplet HISTy{ i f i l e , imax})∗ l ength ( maxsXtriplet HISTx{ i f i l e , imax})==0
maxsXtriplet HISTy{ i f i l e , imax}=0;maxsXtriplet HISTx{ i f i l e , imax}=0;
end ;bar ( maxsXtriplet HISTx{ i f i l e , imax} , maxsXtriplet HISTy{ i f i l e , imax}) ; entr (
i f i l e , C percTDOArefine , nb td+3)=entropy ( maxsXtriplet HISTy{ i f i l e , imax}) ;x l ab e l ( [ num2str (0 .01∗ round (100∗min( maxsXtriplet HISTx{ i f i l e , imax}) ) ) ’ <TDOA,ms< ’
num2str (0 .01∗ round (100∗max( maxsXtriplet HISTx{ i f i l e , imax}) ) ) ] , ’ FontSize ’ , f s i z e ) ;y l ab e l ( [ ’ Number o f a l l maxs TDOAs< ’ num2str (0 .01∗ round (100∗max( maxsXtriplet HISTy{
i f i l e , imax}) ) ) ] , ’ FontSize ’ , f s i z e ) ;t i t l e ( [ maxtypesstr{nb td+3} num2str ( C percTDOArefine ) ’% ’ ’ e=’ num2str (0 .01∗ round
(100∗ entr ( i f i l e , C percTDOArefine , nb td+3) ) ) ] , ’ FontSize ’ , f s i z e )end ;
saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ’TDOA histDT ’ num2str ( [ C percTDOArefine ] ) ’ percSegm ’ num2str ( iSegmentTrajectory )num2str (ovMYmsec) ’ s ta r t ’ num2str ( startMYmsec ) ’ s izeTrack ’ num2str ( s i zeTrack ) ’ . jpg’ ] ) ;
saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ’TDOA histDT ’ num2str ( [ C percTDOArefine ] ) ’ percSegm ’ num2str ( iSegmentTrajectory )num2str (ovMYmsec) ’ s ta r t ’ num2str ( startMYmsec ) ’ s izeTrack ’ num2str ( s i zeTrack ) ’ . eps’ ] , ’ psc2 ’ ) ;
saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ’TDOA histDT ’ num2str ( [ C percTDOArefine ] ) ’ percSegm ’ num2str ( iSegmentTrajectory )num2str (ovMYmsec) ’ s ta r t ’ num2str ( startMYmsec ) ’ s izeTrack ’ num2str ( s i zeTrack ) ’ . f i g’ ] ) ;
c l o s e a l l ;
imagesc ( d e l a i ) ; colormap ( ’ bone ’ ) ; c o l o rba r ;t i t l e ( [ [ num2str (0 .01∗ round (100∗min( maxsXtriplet HISTy{ i f i l e , imax}) ) ) ’ <Number o f
maxsXtr ip lets 3D< ’ num2str (0 .01∗ round (100∗max( maxsXtriplet HISTy{ i f i l e , imax}) ) ) ] ] , ’FontSize ’ , f s i z e ) ;
x l ab e l ( [ num2str (0 .01∗ round (100∗min( maxsXtriplet HISTx{ i f i l e , imax}) ) ) ’ <TDOA,ms< ’num2str (0 .01∗ round (100∗max( maxsXtriplet HISTx{ i f i l e , imax}) ) ) ] , ’ FontSize ’ , f s i z e ) ;
saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ’
124 BIBLIOGRAPHY
TDOA hist3DDT ’ num2str ( [ C percTDOArefine ] ) ’ percSegm ’ num2str ( iSegmentTrajectory )num2str (ovMYmsec) ’ s ta r t ’ num2str ( startMYmsec ) ’ s izeTrack ’ num2str ( s i zeTrack ) ’ .
jpg ’ ] ) ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ’
TDOA hist3DDT ’ num2str ( [ C percTDOArefine ] ) ’ percSegm ’ num2str ( iSegmentTrajectory )num2str (ovMYmsec) ’ s ta r t ’ num2str ( startMYmsec ) ’ s izeTrack ’ num2str ( s i zeTrack ) ’ .
eps ’ ] , ’ psc2 ’ ) ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ’
TDOA hist3DDT ’ num2str ( [ C percTDOArefine ] ) ’ percSegm ’ num2str ( iSegmentTrajectory )num2str (ovMYmsec) ’ s ta r t ’ num2str ( startMYmsec ) ’ s izeTrack ’ num2str ( s i zeTrack ) ’ .
f i g ’ ] ) ;c l o s e a l l ;
s c r s z=get (0 , ’ ScreenSize ’ ) ;f i g u r e ( ’ Pos i t ion ’ , [ 0 0 s c r s z (3 ) s c r s z (4 ) ] ) ;hold on ;p l o t ( vecALL percTDOArefine ( vecALL percTDOArefine<=C percTDOArefine ) , squeeze ( entr ( i f i l e ,
p l o t ( vecALL percTDOArefine ( vecALL percTDOArefine<=C percTDOArefine ) , squeeze ( entr ( i f i l e ,vecALL percTDOArefine ( vecALL percTDOArefine<=C percTDOArefine ) , nb td+2) ) , ’ LineWidth’ , 1 , ’ L ineSty le ’ , ’−− ’ , ’ MarkerSize ’ , msize ∗2 , ’Marker ’ , ’ d ’ ) ;
%p lo t ( vecALL percTDOArefine ( vecALL percTDOArefine<=C percTDOArefine ) , squeeze ( entr ( i f i l e ,vecALL percTDOArefine ( vecALL percTDOArefine<=C percTDOArefine ) , nb td+3) ) , ’ LineWidth’ , 1 , ’ L ineSty le ’ , ’ − . ’ , ’ MarkerSize ’ , msize ∗2 , ’Marker ’ , ’∗ ’ ) ;
t i t l e ( [ ’ Entropy as percntage o f TDOA coherence ’ ] , ’ FontSize ’ , f s i z e ∗2) ;legend ( maxtypesstr{nb td+1} ,maxtypesstr{nb td+2} ,maxtypesstr{nb td+3} , ’ Location ’ , ’ Best
’ , ’ FontSize ’ , f s i zeSm ) ;x l ab e l ( [ num2str (min ( vecALL percTDOArefine ( vecALL percTDOArefine<=C percTDOArefine ) ) ) ’ <
coherentTDOAs/allTDOAs,% < ’ num2str (max( vecALL percTDOArefine (vecALL percTDOArefine<=C percTDOArefine ) ) ) ] , ’ FontSize ’ , f s i z e ∗2) ;
%y l abe l ( [ num2str (0 .01∗ round (100∗min(min (min ( entr ) ) ) ) ) ’ <entropy< ’ num2str (0 .01∗ round(100∗max(max(max( entr ) ) ) ) ) ] , ’ FontSize ’ , f s i z e ∗2) ;
saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ’TDOAENTROPY’ num2str ( [ C percTDOArefine ] ) ’ perc . jpg ’ ] ) ;
saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ’TDOAENTROPY’ num2str ( [ C percTDOArefine ] ) ’ perc . eps ’ ] , ’ psc2 ’ ) ;
saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ’TDOAENTROPY’ num2str ( [ C percTDOArefine ] ) ’ perc . f i g ’ ] ) ;
c l o s e a l l ;end ;
end ;
i f min ( s i z e (plotTDOA{1 , ichant , 1 , 2} ) .∗ s i z e (plotTDOA{1 , ichant , 1 , 3} ) .∗ s i z e (plotTDOA{1 , ichant , 1 , 4} ).∗ s i z e (plotTDOA{1 , ichant , 2 , 3} ) .∗ s i z e (plotTDOA{1 , ichant , 2 , 4} ) .∗ s i z e (plotTDOA{1 , ichant , 3 , 4} ) )==0. . . .
| | min( s i z e (plotTDOA{end , ichant , 1 , 2} ) .∗ s i z e (plotTDOA{end , ichant , 1 , 3} ) .∗ s i z e (plotTDOA{end, ichant , 1 , 4} ) .∗ s i z e (plotTDOA{end , ichant , 2 , 3} ) .∗ s i z e (plotTDOA{end , ichant , 2 , 4} ) .∗ s i z e (plotTDOA{end , ichant , 3 , 4} ) )==0
save ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ’ERRALL.mat’ ] ) ;
m2inCirc=c e l l ( s i z e ( m1inCirc ) ) ;f o r i t r i p l =1: l ength ( m1inCirc )−2
m2inCirc{ i t r i p l } = c i r c s h i f t ( m1inCirc{ i t r i p l } ,[−1 −1]) ;end ;m2inCirc{ l ength ( m1inCirc )−1}=[2 3 4 3 4 4 ] ;m2inCirc{ l ength ( m1inCirc ) }=[2 2 3 4 3 4 4 ] ;f o r C p l o tTr i p l e t s =[ ]
f o r i t r i p l =1:4c l o s e a l ls c r s z=get (0 , ’ ScreenSize ’ ) ;f i g u r e ( ’ Pos i t ion ’ , [ 0 0 s c r s z (3 ) s c r s z (4 ) ] ) ;%v=wavread ( [ globDirIN globEXTpath{ i chant } ] ) ;i f datafrom0cyberio1dodo==0 %ichant<=57
v=wavread ( [ globDirIN globEXTpath{ i chant } ] ) ;e l s e
load ( [ globDirIN globEXTpar . INsubDODO globEXTpath{ i chant } ] ) ;v=tcu r de t ; c l e a r t cu r de t ;
end ;
15. APPENDIX4: MATLAB CODE FOR SIMULATION 125
f o r indmic=1: l ength ( m1inCirc{ i t r i p l })imic=m1inCirc{ i t r i p l }( indmic ) ;[ ysAllTime , fsAllTime , tsAllTime , psAllTime ]= spectrogram (v ( : , imic ) ,256 ,0 ,256 ,250000 , ’
yaxis ’ ) ;
subplot (4 ,3 , indmic ) ; p l o t (v ( 1 : end , imic ) ) ;t i t l e ( [ ’ s i gna l , mic=’ num2str ( imic ) ] , ’ FontSize ’ , f s i zeSm ) ; ax i s o f f ;%y l abe l ( ’ amplitude ’ , ’ FontSize ’ , f s i zeSm ) ; x l ab e l ( ’Temps en s ’ , ’ FontSize ’ , f s i zeSm ) ;
subplot (4 ,3 ,3+ indmic ) ; imagesc ( tsAllTime ( 1 : end ) , fsAllTime , log10 ( abs ( psAllTime (1 :7∗end /8 , : ) ) ) ) ; ax i s xy ; colormap ( f l i p ud ( gray ) ) ;
t i t l e ( [ ’ spectr , mic=’ num2str ( imic ) ] , ’ FontSize ’ , f s i zeSm ) ;y l ab e l ( ’ f req ,Hz ’ , ’ FontSize ’ , f s i zeSm ) ; x l ab e l ( ’ time , s ’ , ’ FontSize ’ , f s i zeSm ) ;
end ;
MAXallTDOAline=−i n f ;MAXallHmaxline=−i n f ;MINallTDOAline=i n f ;MINallHmaxline=i n f ;f o r ind imic =1: l ength ( m1inCirc{ i t r i p l })
imic=m1inCirc{ i t r i p l }( ind imic ) ;jmic=m2inCirc{ i t r i p l }( ind imic ) ;% i f min ( s i z e (plotTDOA)>=[2 ichant imic jmic ] )==0% ERROR=1;% end ;% i f s i z e (plotTDOA{1 , ichant , imic , jmic } ,2)<5 | | s i z e (plotTDOA{1 , ichant ,
p lo t ( plotTIME{ ichant , imic , jmic }/1000 ,plotTDOA{1 , ichant , imic , jmic } ( : , imax ) , ’ Color’ , ’ black ’ , ’ LineWidth ’ , 1 , ’ L ineSty le ’ , ’ − ’ , ’ MarkerSize ’ , msize /1 , ’Marker ’ ,MarkerStr{ imax } , ’ MarkerFaceColor ’ , ’ black ’ ) ;
end ;x l ab e l ( [ ’ time , s ’ ] , ’ FontSize ’ , f s i zeSm ) ; y l ab e l ( [ ’TDOA,ms ’ ] , ’ FontSize ’ , f s i zeSm ) ;t i t l e ( [ ’TDOA( ’ num2str ( vecALL percTDOArefine (1) ) ’ , ’ num2str ( vecALL percTDOArefine (
end ) ) ’%)mics=’ num2str ( [ imic jmic ] ) ] , ’ FontSize ’ , f s i zeSm ) ;%ylim ( [ MINallTDOAline MAXallTDOAline ] ) ; %xlim ( [ x (1) x ( end ) ] ) ;ylim ([−maxTDOAms maxTDOAms] ) ; %xlim ( [ x (1) x ( end ) ] ) ;
subplot (4 ,3 ,9+ i subp l o t ) ; hold on ;f o r imax=1: nb td
end ;x l ab e l ( [ ’ time , s ’ ] , ’ FontSize ’ , f s i zeSm ) ; y l ab e l ( [ ’ Cor .Ampl ’ ] , ’ FontSize ’ , f s i zeSm ) ;t i t l e ( [ ’ Cor .Ampl ( ’ num2str ( vecALL percTDOArefine (1 ) ) ’ , ’ num2str (
vecALL percTDOArefine ( end ) ) ’%)mics=’ num2str ( [ imic jmic ] ) ] , ’ FontSize ’ , f s i zeSm ) ;%ylim ( [ MINallHmaxline MAXallHmaxline ] ) ; %xlim ( [ x (1) x ( end ) ] ) ;ylim ([−maxAmpl maxAmpl ] ) ; %xlim ( [ x (1) x ( end ) ] ) ;
end ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ’
SUMMARYtriplet ’ num2str ( i t r i p l ) ’ iSegm ’ num2str ( iSegmentTrajectory ) ’ . jpg ’ ] ) ;%saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ’
SUMMARYtriplet ’ num2str ( i t r i p l ) ’ iSegm ’ num2str ( iSegmentTrajectory ) ’ . f i g ’ ] ) ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ’
SUMMARYtriplet ’ num2str ( i t r i p l ) ’ iSegm ’ num2str ( iSegmentTrajectory ) ’ . eps ’ ] , ’ psc2 ’ );
saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ’SUMMARYtriplet ’ num2str ( i t r i p l ) ’ iSegm ’ num2str ( iSegmentTrajectory ) ’ . pdf ’ ] ) ;
c l o s e a l l ;end ;
end ;
MarkerStyle ={ ’+ ’ , ’o ’ , ’∗ ’ , ’ x ’ , ’ s ’ , ’ d ’ , ’ ˆ ’ , ’ v ’ , ’ < ’ , ’ > ’ , ’ p ’ , ’ h ’ } ;LineCol={ ’ black ’ , ’ green ’ , ’ blue ’ , ’ red ’ , ’ magenta ’ , ’ black ’ , ’ green ’ , ’ blue ’ , ’ red ’ , ’ magenta ’ , ’ black ’ , ’
green ’ , ’ blue ’ , ’ red ’ , ’ magenta ’ } ;Ncols=3;LineSt={ ’none ’ , ’ − ’} ;x l s1 t27tdoa=NaN∗ones (1+ length ( m1inCirc{ i t r i p l }) , l ength ( vecTimesTracking ) ) ;meanmaxsTDOA=NaN∗ones (1 , l ength ( vecTimesTracking ) ) ; sumTDOAinRange=NaN∗ones (1 , l ength (
vecTimesTracking ) ) ;NtdoaInRange=ze ro s ( l ength ( micro ) , l ength ( micro ) ) ; NtdoaIndOK=zero s ( l ength ( micro ) , l ength ( micro ) ) ;AVtdoaInRange=ze ro s ( l ength ( micro ) , l ength ( micro ) ) ;i t r i p lMa in=length ( m1inCirc )−1;f o r C iLineSt =[1] %[1]
f o r i t r i p l =[ i t r i p lMa in ]%:−1:1c l o s e a l ls c r s z=get (0 , ’ ScreenSize ’ ) ;f i g u r e ( ’ Pos i t ion ’ , [ 0 0 s c r s z (3 ) s c r s z (4 ) ] ) ;ax i s o f f ;i s ubp l o t =0; iC patperc =0; phexist1mic=0; phexistALLmic=1;f o r C patperc=vecALL percTDOArefine
iC patperc=iC patperc +1;i s ubp l o t=i subp l o t +1;subplot (1+ length ( vecALL percTDOArefine ) , Ncols , [ 1 : ( Ncols −1) ] +Ncols ∗( i subp lo t −1) ) ;
hold on ; i l i n e =0;iLegendSty le =0; c l e a r LegendStyle ;LegendStyle=c e l l (1 , l ength ( m1inCirc{ i t r i p l }) ) ;f o r ind imic =1: l ength ( m1inCirc{ i t r i p l })
imic=m1inCirc{ i t r i p l }( ind imic ) ;jmic=m2inCirc{ i t r i p l }( ind imic ) ;i l i n e=i l i n e +1;f o r imax=nb td :−1:1
ph( i l i n e )=p lo t ( plotTIME{ ichant , imic , jmic }/1000 ,plotTDOA{ iC patperc ,ichant , imic , jmic } ( : , imax ) , ’ Color ’ , LineCol{ i l i n e } , ’ LineWidth ’ , 1 , ’L ineSty le ’ , LineSt{C iLineSt } , ’ MarkerSize ’ , msize /C iLineSt , ’ Marker ’ ,MarkerStyle{ i l i n e }) ;
phexist1mic=1;e l s e
phexistALLmic=0;end ;
end ;
iLegendSty le=iLegendSty le +1; LegendStyle{ iLegendSty le }=[ ’ microphones ’ num2str (imic ) ’ and ’ num2str ( jmic ) ] ;
maxstrictTDOAms=2.6; i f i chant==56, maxstrictTDOAms=2.4; end ;
i f l ength ( TIMEinplotrange )>=2xl s1 t27tdoa ( indimic , : )=in t e rp1 ( TIMEinplotrange , TDOAinplotrange , plotTIME{
ichant , imic , jmic } , ’ cubic ’ ) ;% , ’ extrap ’x l s1 t27tdoa ( 1 , : )=plotTIME{ ichant , imic , jmic } ;%PLOTLINES ph( i l i n e +1)=p lo t ( x l s1 t27tdoa (1 , indOK(1) : indOK( end ) ) /1000 ,
x l s1 t27tdoa ( indimic , indOK(1) : indOK( end ) ) , ’ Color ’ , LineCol{ i l i n e } , ’LineWidth ’ , 1 , ’ L ineSty le ’ , ’ − ’ , ’Marker ’ , ’ none ’ ) ;
end ;
end ;x l ab e l ( [ ’ time , s ’ ] , ’ FontSize ’ , f s i zeSm ) ; y l ab e l ( [ ’TDOA,ms ’ ] , ’ FontSize ’ , f s i zeSm ) ;xlim ( [ ( vecTimesTracking (1) −0.0001) /1000 ( vecTimesTracking ( end ) +0.0001) /1000 ] ) ;RecName=globEXTcourt{ i chant } ;RecName(RecName==’ ’ ) =’− ’; i f C patperc <99, s t rC patperc=num2str ( C patperc ) ; e l s e
s t rC patperc=num2str (100) ; end ;strNtdoa =[ ’ Ntdoa=’ num2str ( [ NtdoaInRange (1 ,2 ) NtdoaInRange (1 ,3 ) NtdoaInRange (1 , 4 )
NtdoaIndOK(2 ,3 ) NtdoaIndOK(2 ,4 ) NtdoaIndOK(3 ,4 ) ] ) ] ;t i t l e ( [ RecName ’ ’ ALLdescrStrCour ’ TDOA( ’ s t rC patperc ’%) Ant ’ num2str (maxTDOAm)
vecALL percTDOArefine ) ) ) ;p=get ( sh , ’ po s i t i on ’ ) ; %lh=legend ( sh , [ ph1 ; ph2 ] ) ;i f phexist1mic && phexistALLmic && length (ph)==length ( m1inCirc{ i t r i p l }) && length (
c l o s e a l l ;x l sw r i t e ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant }
ALLdescrStr ’TR’ num2str ( i t r i p l ) ’ vPerc ’ num2str ( vecALL percTDOArefine ) ’ t ’ num2str (C iLineSt ) ’ j13 . x l s ’ ] , x l s1 t27tdoa ) ;
endend
i f isempty ( C iLineSt )x l s1 t27tdoa = x l s r ead ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{
i chant } ALLdescrStr ’TR’ num2str ( i t r i p lMa in ) ’ vPerc ’ num2str ( vecALL percTDOArefine ) ’ t ’num2str ( C iLineSt ) ’ j13 . x l s ’ ] ) ;
end ;NlessANTEN=sum( abs ( x l s1 t27tdoa ( 2 : end , : ) )<maxTDOAms, 1 ) ;NlessANTENmore=sum( abs ( x l s1 t27tdoa ( 2 : end , : ) )<maxTDOAms & abs ( x l s1 t27tdoa ( 2 : end , : ) )>maxTDOAms
/10 ,1) ;% −− ranse b r a l i vse , a ne tak kak da l ee too lko NlessANTENmoreMeanMic=mean( x l s1 t27tdoa ( 2 : end , NlessANTENmore>0) ,1) ;StdMic=std ( x l s1 t27tdoa ( 2 : end , NlessANTENmore>0) ,1) ;%MaxMic=max( x l s1 t27tdoa ( 2 : end , NlessANTENmore>0) , [ ] , 1 ) ;%MinMic=min( x l s1 t27tdoa ( 2 : end , NlessANTENmore>0) , [ ] , 1 ) ;
Ntdoa2D{1}( ichant , indC DivsMaxsPers )=sq r t ( t o t a lE r r o r ( ichant , iSegmentTrajectory , indC DivsMaxsPers) ) ;
Ntdoa2D{2}( ichant , indC DivsMaxsPers )=Nt r i p l e t s ( ichant , iSegmentTrajectory , indC DivsMaxsPers ) ;Ntdoa2D{3}( ichant , indC DivsMaxsPers )=sq r t ( tota lErrorMin ( ichant , iSegmentTrajectory ,
indC DivsMaxsPers ) ) ;
Ntdoa2D{4}( ichant , indC DivsMaxsPers )=sq r t ( totalErrorMax ( ichant , iSegmentTrajectory ,indC DivsMaxsPers ) ) ;
C DivsMaxsPers==ALL5hz1w1maxs2pers2{ iSegmentTrajectory } , 1 , ’ f i r s t ’ ) ;Ntdoa2D{8}( ichant , indC DivsMaxsPers )=sum( StdMic ) ;Ntdoa2D{9}( ichant , indC DivsMaxsPers )=100∗sum(NlessANTEN==0)/ s i zeTrack ;Ntdoa2D{10}( ichant , indC DivsMaxsPers )=100∗sum(NlessANTENmore==0)/ s i zeTrack ;
%Ntdoa2D{20}( ichant , indC DivsMaxsPers )=std ( plotTDOAall{ ichant , imic , jmic }) ;%Ntdoa2D{21}( ichant , indC DivsMaxsPers )=std ( plotTIMEall{ ichant , imic , jmic }) ;end ;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% END CORRELATION OF SIGNALS AND REFINE TDOA TIME CALCULATION%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
15. APPENDIX4: MATLAB CODE FOR SIMULATION 129
end ;
end ;
end ;save ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ALLdescrStrCour ’
iSegm ’ num2str ( iSegmentTrajectory ) ’Ntdoa2D . perBYmax .mat ’ ] , ’ Ntdoa2D ’ , ’ t o ta lEr ro r ’ , ’totalTime ’ , ’ Nt r i p l e t s ’ , ’ totalErrorMin ’ , ’ totalErrorMax ’ , ’ totalErrorN ’ ) ;
f o r C plot2Darr DivsMaxsPers =[1]Ntdoa2Dstr={ ’ t o ta lEr ro r ’ , ’ Nt r i p l e t s ’ , ’ totalErrorMin ’ , ’ totalErrorMax ’ , ’ Nt r i p l e t s ’ , ’ refinedTDOA/
sizeTrack ’ , ’ MEANnotStatic over micros ’ , ’ STDnotStatic over micros ’ , ’ Percent o f l e s sAnt dts ,% ’ , ’ Percent o f void l e s sAntNotStat i c dts , % ’ , ’ refinedTDOA/ sizeTrack , notStat i c ’ , ’ ’ , ’ Rangeover a l l microphones ’ , ’ Range−STD over a l l microphones’ , ’ ; ’ , ’ ; ’ , ’ ; ’ , ’ ; ’ , ’ ; ’ , ’ ; ’ , ’ ; ’ , ’ ; ’ , ’ ; ’ , ’ ; ’ , ’ ; ’ , ’ ; ’ , ’ ; ’ , ’ ; ’ , ’ ; ’ , ’ ; ’ , ’ ; ’ , ’ ; ’ , ’ ; ’ } ;
f o r i chant=ALLbatNumsf o r i o i =1:N2Darr DivsMaxsPersi i =0;MAXiAllmaxs=−i n f ∗ones (1 , N2Darr DivsMaxsPers ) ; MAXiAllperc=−i n f ∗ones (1 , N2Darr DivsMaxsPers ) ;
MAXii=−i n f ∗ones (1 , N2Darr DivsMaxsPers ) ; MAXvalue=−i n f ∗ones (1 , N2Darr DivsMaxsPers ) ;f o r iAl lmaxs=1: l ength (ALLmaxs)f o r iA l l p e r c =1: l ength (ALLperc )
i i= i i +1;ALLmaxsBYperc{ i o i , iSegmentTrajectory }( iAllmaxs , iA l l p e r c )=Ntdoa2D{ i o i }( ichant , i i ) ;i f MAXvalue( i o i )<ALLmaxsBYperc{ i o i , iSegmentTrajectory }( iAllmaxs , iA l l p e r c )
MAXiAllmaxs ( i o i )=iAllmaxs ; MAXiAllperc ( i o i )=iA l l p e r c ; MAXii( i o i )=i i ; MAXvalue( i o i )=ALLmaxsBYperc{ i o i , iSegmentTrajectory }( iAllmaxs , iA l l p e r c ) ;
end ;end ;end ;% imagesc (ALLmaxsBYperc{ i o i , iSegmentTrajectory }) ; c o l o rba r ;% x l abe l ( ’%TDOA+1 ’ , ’ FontSize ’ , f s i z eB i g ) ; %x l abe l ( ’ windowSize/ timeStep ’ , ’
FontSize ’ , f s i z eB i g ) ;% y l abe l ( ’numberTDOAmaxs/2 ’ , ’ FontSize ’ , f s i z eB i g ) ; %y l abe l ( ’ ClickFreq , 5Hz (5/
timeStep ) ’ , ’ FontSize ’ , f s i z eB i g ) ;% t i t l e ( Ntdoa2Dstr{ i o i } , ’ FontSize ’ , f s i z eB i g ) ;% saveas ( gcf , [ globDirOUT globEXTpar .OUTsub globEXTcourt{ i chant } num2str ( i o i )
ALLdescrStrCour ’ iSegm ’ num2str ( iSegmentTrajectory ) ’ . perBYmaxALL . jpg ’ ] ) ;% %saveas ( gcf , [ globDirOUT globEXTpar .OUTsub globEXTcourt{ i chant } num2str ( i o i
) ALLdescrStrCour ’ iSegm ’ num2str ( iSegmentTrajectory ) ’ . perBYmaxALL . f i g ’ ] ) ;% saveas ( gcf , [ globDirOUT globEXTpar .OUTsub globEXTcourt{ i chant } num2str ( i o i )
ALLdescrStrCour ’ iSegm ’ num2str ( iSegmentTrajectory ) ’ . perBYmaxALL . eps ’ ] , ’ psc2 ’ ) ;% saveas ( gcf , [ globDirOUT globEXTpar .OUTsub globEXTcourt{ i chant } num2str ( i o i )
ALLdescrStrCour ’ iSegm ’ num2str ( iSegmentTrajectory ) ’ . perBYmaxALL . pdf ’ ] ) ;% c l o s e a l l ;end ;end ;save ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ALLdescrStrCour ’
TDOA(mic2−mic4 ) ’ , ’TDOA(mic3−mic4 ) ’ , ’ sumNtdoa by sizeTrack ’ , ’ Ntdoa in Range ’ , ’N t r i p l e t s i n e r r o r c a l c u l a t i o n ’ , ’ sum of tdoa ’ , ’ Nt r i p l e t s ’ , ’ t o ta lEr ro r ’ , ’ totalTimeMean ’ , ’
i f i snan (nanmean(nanmin ( cube ( iSegmentTrajectory , : , 1 4 ) ,2 ) ) )squareRes ( iSegmentTrajectory , 1 5 ) =0;e l s esquareRes ( iSegmentTrajectory , 1 5 )=nanmean(nanmin ( cube ( iSegmentTrajectory , : , 1 4 ) ,2 ) ) ;end ;
i f i snan (nanmean(nanmax( cube ( iSegmentTrajectory , : , 1 4 ) ,2 ) ) )squareRes ( iSegmentTrajectory , 1 6 ) =0;e l s esquareRes ( iSegmentTrajectory , 1 6 )=nanmean(nanmax( cube ( iSegmentTrajectory , : , 1 4 ) ,2 ) ) ;end ;
i f i snan (nanmean(nansum( cube ( iSegmentTrajectory , : , 1 4 ) ,2 ) ) )squareRes ( iSegmentTrajectory , 1 7 ) =0;e l s esquareRes ( iSegmentTrajectory , 1 7 )=nanmean(nansum( cube ( iSegmentTrajectory , : , 1 4 ) ,2 ) ) ;end ;
end ;save ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ALLdescrStrCour ’
CUBEmars ’ num2str (ALLmaxs) ’ . mat ’ ] ) ;
a l lNtdoa =[8:10 1 2 ] ;xlsTXTnew=ze ro s ( s i z e ( cube , 1 ) ,2∗ s i z e ( cube , 2 ) ) ;f o r i=al lNtdoaf o r time=1: s i z e ( cube , 1 )xlsTXTnew( time , : ) =[cube ( time , : , 1 ) cube ( time , : , i ) ] ;x l sw r i t e ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant }
ALLdescrStrCour s t rLas t Ind { i } ’TXTnew. x ls ’ ] , xlsTXTnew) ;save ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ALLdescrStrCour
s t rLas t Ind { i } ’TXTnew.mat ’ ] , ’ xlsTXTnew ’ ) ;endend
al lNtdoa = [ 1 : 1 3 ] ;xlsTXTcubsq=ze ro s ( s i z e ( cube , 1 ) ,2 ) ;f o r i=al lNtdoaf o r time=1: s i z e ( cube , 1 )xlsTXTcubsq ( time , : ) =[cube ( time , 1 , i ) squareRes ( time , i ) ] ;x l sw r i t e ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant }
ALLdescrStrCour s t rLas t Ind { i } ’TXTcubsq . x l s ’ ] , xlsTXTcubsq ) ;save ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ALLdescrStrCour
s t rLas t Ind { i } ’TXTcubsq .mat ’ ] , ’ xlsTXTcubsq ’ ) ;endend
f o r w i l lDe l=0f i g u r e ; bar ( squeeze ( cube ( : , : , 1 ) ) ./(1+ w i l lDe l ∗cube ( : , : , 8 ) ) /1000)bar l egends=c e l l (1 , l ength (ALLmaxs) ) ;f o r i l e g =1: l ength (ALLmaxs) , bar l egends { i l e g }=[num2str (ALLmaxs( i l e g ) ) ’max ’ ] ; end ;legend ( bar l egends ) ;t i t l e ( ’ Advantage o f us ing mul t ip l e maxs over s i n g l e max ’ ) ;y l ab e l ( ’ t o t a l TDOA error ,ms ’ ) ;x l ab e l ( [ ’ time−moment , s ∗ ’ num2str ( Lseg /1000) ] ) ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant }
ALLdescrStrCour num2str ( w i l lDe l ) ’TDOAerrorCUBE . jpg ’ ] ) ;c l o s e a l l ;end ;
15. APPENDIX4: MATLAB CODE FOR SIMULATION 131
f i g u r e ; bar ( [ cube ( : , 1 , 1 ) squareRes ( : , 1 ) ] /1000)legend ( ’ s tate−of−the−art ’ , ’ our r e su l t ’ ) ;t i t l e ( ’ Comparison o f State−of−the−ar t with our r e su l t ’ ) ;y l ab e l ( ’ t o t a l TDOA error ,ms ’ ) ;x l ab e l ( [ ’ time−moment , s ∗ ’ num2str ( Lseg /1000) ] ) ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant }
ALLdescrStrCour num2str ( w i l lDe l ) ’TDOAerrorRESULT . jpg ’ ] ) ;c l o s e a l l ;
a l lNtdoa=[14 15 16 1 7 ] ;f o r j =1: l ength (ALLmaxs)f o r i=al lNtdoaf i g u r e ; bar ( [ cube ( : , j , i ) squareRes ( : , i ) ] )l egend ( ’ s tate−of−the−art ’ , ’ our r e su l t ’ ) ;t i t l e ( ’ Comparison o f State−of−the−ar t with our r e su l t ’ ) ;y l ab e l ( ’ t o t a l TDOA ca l c u l a t i o n time , s ’ ) ;x l ab e l ( [ ’ time−moment , s ∗ ’ num2str ( Lseg /1000) ] ) ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant }
ALLdescrStrCour ’ Nmaxs=’ num2str (ALLmaxs( j ) ) ’ ’ s t rLas t Ind { i } ’ TDOAtimeRESULT. png ’ ] ) ;c l o s e a l l ;end ;end ;
f o r imax=1: s i z e ( cube , 2 )f i g u r e ; f i g u r e ; p l o t ( squeeze ( cube ( : , imax , 2 : 7 ) ) , ’ marker ’ , ’ x ’ ) ; %bar ( squeeze ( cube ( : , imax , 2 : 7 ) ) , ’
stack ’ ) wlegend ( s t rLas t Ind {2 :7} ) ;t i t l e ( [ ’TDOAs of ’ num2str ( imax ) ’max , ’ ’N=’ num2str ( cube ( : , imax , 8 ) ’ ) ] ) ;y l ab e l ( ’TDOA,ms ’ ) ;x l ab e l ( [ ’ time−moment , s ∗ ’ num2str ( Lseg /1000) ] ) ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant }
ALLdescrStrCour ’TDOA’ num2str ( imax ) ’ m a xCUBE . jpg ’ ] ) ;c l o s e a l l ;end ;
f i g u r e ; p l o t ( squeeze ( squareRes ( : , 2 : 7 ) ) , ’ marker ’ , ’ x ’ ) ; %bar ( squeeze ( squareRes ( : , 2 : 7 ) ) , ’ stack ’ )legend ( s t rLas t Ind {2 :7} ) ;t i t l e ( [ ’TDOAs o f our r e s u l t s ’ ] ) ;y l ab e l ( ’TDOA,ms ’ ) ;x l ab e l ( [ ’ time−moment , s ∗ ’ num2str ( Lseg /1000) ] ) ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant }
ALLdescrStrCour ’TDOA’ ’ourRESULT . jpg ’ ] ) ;c l o s e a l l ;
f i g u r e ; p l o t ( squeeze ( cube ( : , 1 , 2 : 7 ) ) , ’ marker ’ , ’ x ’ ) ; %bar ( squeeze ( squareRes ( : , 2 : 7 ) ) , ’ stack ’ )legend ( s t rLas t Ind {2 :7} ) ;t i t l e ( [ ’TDOAs o f s t a t e o f the art ’ ] ) ;y l ab e l ( ’TDOA,ms ’ ) ;x l ab e l ( [ ’ time−moment , s ∗ ’ num2str ( Lseg /1000) ] ) ;saveas ( gcf , [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant }
ALLdescrStrCour ’TDOA’ ’ baselineRESULT . jpg ’ ] ) ;c l o s e a l l ;
save ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ALLdescrStrCour ’ALLSegm . perBYmax .mat ’ ] , ’ Ntdoa2D ’ , ’ t o ta lEr ro r ’ , ’ totalTime ’ ) ;
load ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ALLdescrStrCour ’ALLSegm . perBYmax .mat ’ ] ) ;
save ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ALLdescrStrCour ’CUBEmars ’ num2str (ALLmaxs) ’ maxfromDelai10 .mat ’ ] , ’ cube ’ , ’ squareRes ’ ) ;
MarkerStyle ={ ’+ ’ , ’o ’ , ’∗ ’ , ’ x ’ , ’ s ’ , ’ d ’ , ’ ˆ ’ , ’ v ’ , ’ < ’ , ’ > ’ , ’ p ’ , ’ h ’ } ;LineCol={ ’ black ’ , ’ green ’ , ’ blue ’ , ’ red ’ , ’ magenta ’ , ’ black ’ , ’ green ’ , ’ blue ’ , ’ red ’ , ’ magenta ’ , ’ black ’ , ’
green ’ , ’ blue ’ , ’ red ’ , ’ magenta ’ } ;Ncols=3;LineSt={ ’none ’ , ’ − ’} ;i t r i p l=i t r i p lMa in ;f o r i chant=ALLbatNumsc l o s e a l ls c r s z=get (0 , ’ ScreenSize ’ ) ;
132 BIBLIOGRAPHY
f i g u r e ( ’ Pos i t ion ’ , [ 0 0 s c r s z (3 ) s c r s z (4 ) ] ) ;ax i s o f f ;i s ubp l o t =0; iC patperc =0; phexist1mic=0; phexistALLmic=1; al lTplotTIMEal l = [ ] ;f o r C patperc=vecALL percTDOArefine ( end )iC patperc=iC patperc +1;i s ubp l o t=i subp l o t +1;subplot (1+ length ( vecALL percTDOArefine ) , Ncols , [ 1 : ( Ncols −1) ] +Ncols ∗( i subp lot −1) ) ; hold on ; i l i n e
=0;iLegendSty le =0; c l e a r LegendStyle ;LegendStyle=c e l l (1 , l ength ( m1inCirc{ i t r i p l }) ) ;f o r ind imic =1: l ength ( m1inCirc{ i t r i p l })imic=m1inCirc{ i t r i p l }( ind imic ) ;jmic=m2inCirc{ i t r i p l }( ind imic ) ;i l i n e=i l i n e +1;i f s i z e ( plotTDOAall{ ichant , imic , jmic } ,2)∗ s i z e ( plotTIMEall{ ichant , imic , jmic } ,2) && ˜ isempty (
plotTDOAall{ ichant , imic , jmic }) && ˜ isempty ( plotTIMEall{ ichant , imic , jmic })ph1 ( i l i n e )=p lo t ( plotTIMEall{ ichant , imic , jmic }/1000 , plotTDOAall{ ichant , imic , jmic } , ’ Color ’ , LineCol
{ i l i n e } , ’ LineWidth ’ , 1 , ’ L ineSty le ’ , LineSt{C iLineSt } , ’ MarkerSize ’ , msize /C iLineSt , ’ Marker ’ ,MarkerStyle{ i l i n e }) ;
phexist1mic=1; al lTplotTIMEal l=[ al lTplotTIMEal l plotTIMEall{ ichant , imic , jmic } ] ;phexist1mic=1;e l s ephexistALLmic=0;end ;iLegendSty le=iLegendSty le +1; LegendStyle{ iLegendSty le }=[ ’ microphones ’ num2str ( imic ) ’ and ’
num2str ( jmic ) ] ;maxstrictTDOAms=2.6; i f i chant==56, maxstrictTDOAms=2.4; end ;end ;x l ab e l ( [ ’ time , s ’ ] , ’ FontSize ’ , f s i zeSm ) ; y l ab e l ( [ ’TDOA,ms ’ ] , ’ FontSize ’ , f s i zeSm ) ;xlim ( [ AllSegmentTrajectoryMS {1}(1) /1000 AllSegmentTrajectoryMS{end }( end ) /1000 ] ) ;RecName=globEXTcourt{ i chant } ;RecName(RecName==’ ’ ) =’− ’; i f C patperc <99, s t rC patperc=num2str ( C patperc ) ; e l s e s t rC patperc=
num2str (100) ; end ;strNtdoa = [ ’ ’ ] ;strIndOK = [ ’ ’ ] ;t i t l e ( [ RecName ’ ’ ALLdescrStrCour ’ on ’ num2str ( AllSegmentTrajectoryMS {1}) ’ : ’ s t r edge ’ on ’
num2str ( AllSegmentTrajectoryMS {2}) ’ : ’ strmid ’ on ’ num2str ( AllSegmentTrajectoryMS {3}) ’ : ’s t r edge ’ Ant ’ num2str (maxTDOAm) ’m’ ] ) ;
ylim ([−maxTDOAms maxTDOAms] ) ;endi subp l o t=i subp l o t +1; subplot (1+ length ( vecALL percTDOArefine ) , Ncols , [ 1 : ( Ncols −1) ] +Ncols ∗( i subp lot
−1) ) ;%v=wavread ( [ globDirIN globEXTpath{ i chant } ] ) ;i f datafrom0cyberio1dodo==0 %ichant<=57v=wavread ( [ globDirIN globEXTpath{ i chant } ] ) ;e l s eload ( [ globDirIN globEXTpar . INsubDODO globEXTpath{ i chant } ] ) ;v=tcu r de t ; c l e a r t cu r de t ;end ;p l o t ( l i n s pa c e ( AllSegmentTrajectoryMS {1}(1) /1 , AllSegmentTrajectoryMS{end }( end ) /1 ,
INDvecTimesTracking ( end )−INDvecTimesTracking (1)+1)/1000 , v ( INDvecTimesTracking (1) :INDvecTimesTracking ( end ) , m1inCirc{ i t r i p l }) , ’ k ’ ) ;
x l ab e l ( [ ’ time , s ’ ] , ’ FontSize ’ , f s i zeSm ) ; y l ab e l ( [ ’ Recording Amplitude ’ ] , ’ FontSize ’ , f s i zeSm ) ;xlim ( [ AllSegmentTrajectoryMS {1}(1) /1000 AllSegmentTrajectoryMS{end }( end ) /1000 ] ) ;sh1=subplot (1+ length ( vecALL percTDOArefine ) , Ncols , Ncols ) ;%: Ncols : Ncols∗(1+ length (
vecALL percTDOArefine ) ) ) ;p1=get ( sh1 , ’ po s i t i on ’ ) ; %lh=legend ( sh , [ ph1 ; ph2 ] ) ;i f phexist1mic && phexistALLmic && length ( ph1 )==length ( m1inCirc{ i t r i p l }) && ˜ isempty (
al lTplotTIMEal l ) %e a r l i e r : && length ( TIMEinplotrange )>=2 && length (TDOAinplotrange )>=2NcurvesOK=min( length ( ph1 ) , l ength ( LegendStyle ) ) ;lh1=legend ( sh1 , ph1 ( 1 : NcurvesOK) , LegendStyle ( 1 : NcurvesOK) ) ; % %lh=legend ( LegendStyle ) ; % % % % %
% % %se t ( lh1 , ’ po s i t i on ’ , p1 ) ;ax i s ( sh1 , ’ o f f ’ ) ;end ;
c l o s e a l l ;x l sw r i t e ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ALLdescrStr ’
TR’ num2str ( i t r i p l ) ’ vPerc ’ num2str ( vecALL percTDOArefine ) ’ t ’ num2str ( C iLineSt ) ’ j13 . x l s’ ] , x l s1 t27tdoa ) ;
x l sw r i t e ( [ globDirOUT globEXTpar .OUTsub BegFi le num2str ( C cor ) globEXTcourt{ i chant } ALLdescrStr ’TR’ num2str ( i t r i p l ) ’ vPerc ’ num2str ( vecALL percTDOArefine ) ’ t ’ num2str ( C iLineSt ) ’ j13 . x l s’ ] , x l s1 t27tdoa ) ;
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