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A Combined Broadband Single-Beam and
Narrowband Split-Beam SONAR System
Jae-Byung Jung, Member, IEEE, Alexander B. Kulinchenko, Member, IEEE,
Patrick K. Simpson, Senior Member, IEEE and James W. Tilley, Member, IEEE
Abstract
In this paper, we present a recent development in which the capabilities of a broadband single-beam and
narrowband split-beam sonar system are integrated into a single system. This development not only provides an
efficient tool to accommodate the benefits of both split-beam processing for target tracking (e.g. fish counting)
and broadband processing for target identification (e.g. species identification), but also takes advantages of the
synergism of both. Specifically, split-beam processing provides accurate angular information for targets within
the ensonified coverage. The targets angular information is used in broadband processing to compensate for the
irregularity of broadband spectrum caused by inherent uneven sensitivity of transducer across the effective beam
angle. Simultaneously, exceptional range resolution derived by broadband pulse compression provides improvement
over narrowband target resolution. Signal processing also allows a broadband sonar to emulate multiple narrowbandsonar systems by applying an instantaneous time-frequency representation. A series of tests under both controlled
conditions at a lakeside acoustic test facility and in operational environments on rivers have been conducted.
Index Terms
Broadband, narrowband, single-beam, split-beam, fisheries, sonar, neural network, classification, time-frequency
representation.
I. INTRODUCTION
A
S the number of fish in rivers and streams diminishes and become threatened, endangered, or extinct,
we see a growing concern from the local communities that is being met with increased funding and
political attention. There is a need for better fish monitoring tools for the riverine environment.
The primary tools for monitoring fish passage in an area where there are man-made impediments to
travel are tags and sonars. There are many tagging solutions [48], ranging from sonar tags for actively
following a fish [29], to clipping of a fin for later identification [2]. Sonar systems are used predominantly
for assessing the quantity and behavior at key points along a fishs path [25][26].
Split-beam sonar has a transducer that is typically divided into four quadrants. The target detection is
determined by comparing the echoes received from all quadrants. Using the phase difference between the
signals received by appropriate sections allows the target to be located within the beam [21].
In the shallow water riverine environments, the sonars ping rate is set very high to provide multiple
reflections from a single target and facilitate tracking. Each target detection is passed to a fish tracking
routine that aggregates successive pings in the same or adjoining range cells into a track of the fishs path
through the sonar beam [3][4][5]. Tracking fish is also the primary method of fish counting in riverine
environments such as those found in Alaska and Canada. This information is the primary tool used for
determining the amount of the fish runs [8][46][70][71].
Although the narrowband split-beam systems can monitor the movement of fish tracks and estimate the
size of targets by calculating target strength, target identification and spatial range resolution can be very
limited with these systems.
Jae-Byung Jung, Alexander B. Kulinchenko, and Patrick K. Simpson are with Scientific Fishery Systems, Inc., Anchorage AK 99516
(email: [email protected]; [email protected]; [email protected]).
James W. Tilley is with Alaska Native Technologies, LLC., Anchorage AK 99516 (email: [email protected]
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The primary advantages of broadband sonar over the existing narrowband systems includes: (1) the
broad spectral information for species identification, (2) the improved target detection, (3) more stable
estimate of signal, and (4) improved spatial resolution.
As a well-established theorem in information theory that holds that the information-carrying capacity of
a communications channel depends on its bandwidth, the broadband acoustic data processing is expected
to be a productive approach for species identification with the additional assistance of the appropriate
analytical tools such as neural networks, expert systems, and new methods of discriminant analysis
[42][43].
Broadband systems also exploit time-bandwidth product of pulse compression to produce excellent
spatial resolution as well as superior signal to noise ratio for improved target detection and stable signal
estimate over conventional narrowband systems.
Through the application of instantaneous time-frequency representation (ITFR) algorithms, it is possible
to emuluate multi-channel narrowband spectral responses at every discrete sample from a broadband
signal. Various kernels have been investigated to acquire better temporal and spectral resolution with less
interference [9][10][28][44][72].
Neural networks have been used to achieve over 80 % correct discrimination among frequency spectra
for echoes from different species of fish [52] and zooplankton [68]. The results of several independent
field tests have shown that broadband sonar provides a promising approach for discriminating various fish
species using either (or both) spatial and spectral features [22] [31][32][33][34][35][51][56][60].
In addition to the individual benefits of narrowband split-beam and broadband single-beam systems,
the combination of these two capabilities in a single system supplies mutually advantageous cooperative
functions. The accurate target location realtive to the Maximum Response Axis (MRA) provided by the
split-beam portion of the system provides the information needed to compensate for the uneven broadband
beam patterns. Conversely, the improved range resolution from broadband pulse compression helps split-
beam tracks be distinguished clearly. Technical details of thsee synergistic combinations are discussed in
the sections V and VI.
From a practical point of view, especially for riverine fish migration monitoring, sonar systems are
often deployed in very remote and secluded locations, often with great distances between the wet-end of
the sonar where the transducer is housed, and the dry-end of the system where data is stored, power is
provided, and communications with the system are established. Under such conditions, the wet-end (sonar
head) and the dry-end (monitoring station) can be isolated by natural barriers and be required to cover
a long distance. Therefore, analog sensor signal conditioning, such as band-pass filter and amplification,
and analog-to-digital conversion as an initial step of data acquisition should be processed as early as
possible in the wet-end sensor unit to prevent signal contamination from various noise sources during
the transmission. Furthermore, the up front digitization and full digital signal processing in the wet-end
unit allows digital data transfer in wired or wireless TCP/IP, providing additional flexibility in remote
environments.
The following sections discuss the overall architectural design of the system, details of system devel-
opment, newly proposed data processing methods utilizing both narrowband and broadband processings,
and various field tests for system evaluation.
I I . FUNCTIONAL REQUIREMENTS
A novel sonar system combining broadband single-beam and narrowband split-beam is designed to
identify, count, and track migratory fish stocks for use in shallow water environments.
The key requirements that were incorporated when combining the capabilities of each sonar system
include the following:
Switch between broadband and narrowband,
Switch between single-beam and split-beam,
Interleaving broadband single-beam and narrowband split-beam,
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Programmable transmit power,
Programmable receive gain,
Programmable waveform,
Wet-end signal conditioning and data acquisition,
Network connectivity through TCP/IP, and
Compatibility with existing softwares.
When the sonar is operated as a single beam system, it can transmit broadband pulses or multi-frequencynarrowband pulses. This mode of operation is primarily used for determining species or size of the targets,
such as fish, bubbles, and debris. When the sonar is operated as a split-beam system, the transmit frequency
is fixed at the optimal frequency for transducer performance. This mode of operation provides split-beam
processing for tracking targets as they move through the beam.
Fig. 1. Interleaving ping patterns: 6 examples of flexible ping scheduling schemes are shown; narrowband pings only, 3 narrowband pings
and 1 broadband ping, 2 narrowband pings and 2 broadband pings, 1 narrowband ping and 3 broadband pings, 1 narrowband ping and 1
broadband ping, and broadband pings only, respectively.
Furthermore, the system can change sonar parameters on a ping-by-ping basis allowing several shorter
narrowband pulses in the split-beam mode to be interleaved with a smaller number of the longer broadband
(or multi-frequency) pulses. Each sequence of interleaving pulses can be defined so that it provides
adequate narrowband coverage for target tracking and provides additional broadband target identification.Fig. 1 includes six examples of ping scheduling schemes, including (1) narrowband pings only, (2) three
narrowband pings and one broadband ping, (3) two narrowband pings followed by two broadband pings,
(4) one narrowband ping followed by three broadband pings, (5) one narrowband ping followed by one
broadband ping, and (6) broadband pings only, respectively.
III. HARDWARE ARCHITECTURE
The combined split-beam/broadband sonar system consists of three primary units: wet-end system; dry-
end system; and underwater cable. The wet-end system is deployed in the water and is the main sonar
unit with the computational resources and sensor module enclosed in an underwater housing. The dry-end
system remains shore side. Its primary functions include power supply to wet-end system, communication
with wet-end system, and wired or wireless TCP/IP with external computers or data storage. An underwatercable connects both wet-end and dry-end units together over a distance of as much as 100 m. Although
all three units are needed and must function seamlessly together, we would like to focus our discussion
on wet-end system in this section.
The wet-end consists of three primary system segments: client PC, controller, and transducer as is
illustrated in Fig. 2.
A. Client PC Segment
This is the main processing unit performing data acquisition, signal processing, and communication
with the dry-end.
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A low power single board computer (SBC) with capability of floating point computation executes
signal processing tasks in coordination with an optional digital signal processing (DSP) board. One of the
available serial ports (RS-232) in SBC transmits control commands to the Controller Segmentto all changes
to the transmit power and receive gain. An integrated ethernet (TCP/IP) also allows communication with
dry-end system.
Data acquisition board passes on the waveform to Controller Segment so that it can be amplified
according to the transmit power setup by SBC. At the same time, analog-digital conversions are executed
in 4 separate channels simultaneously according to the receive gain setup by SBC.
Once all the data processing tasks were completed, the data sets are either saved in the internal data
storage or transferred to the dry-end system through TCP/IP in real-time.
Fig. 2. The wet-end hardware architecture: component modules consist of client PC segment, control segment, and transducer segment.
DC power lines and category-5 lines are fed from dry-end via underwater cable.
B. Controller Segment
This segment consists of custom electronics boards that have been developed exclusively for digital
control, power distribution, transmit waveform amplification, and receive signal amplification.
A field programmable gate array (FPGA) communicates with the SBC to control 16 programmable
transmit power levels and to adjust 16 programmable receive gain levels to remain flexible for eachdeployment environment. Based upon the software configuration, the embedded analog power management
module distributes the appropriate DC power levels to each of the functional modules across the entire
wet-end system.
Analog transmit (TX) module sends an amplified analog waveform to the transducer for signal projection
in the water column. The analog receive (RX) module receives the raw signals in four separate hardware
channels from each of the transducers quadrants and then executes the initial signal conditioning in
hardware including preamplification, band pass filtering, and the application of signal gain.
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C. Transducer Segment
The transducer has four equally split quadrants that provide the ability to perform split-beam processing.
Although each of the channels are recorded independently at reception, all quadrants simultaneously
transmit the same waveform at the same power level for a composite conical beam projection.
IV. SIGNAL PROCESSING
The data processing paths vary depending on the mode of operation for that ping: broadband single-beam or narrowband split-beam. Each of these data processing paths has its own processing flow as well
as assistance from its counter part. Fig. 3 shows the block diagram of the overall data processing chain
for the received signal.
Fig. 3. Overall data processing chain: First 4 blocks in each quadrant represent hardware implemented functions for initial common
processing and the remainder of the blocks are software processed functions.
The processing can be partitioned into five functional divisions: (1) the common hardware processing
upon the signal reception, (2) channel mixer, (3) narrowband split-beam processing, (4) broadband single-
beam processing, and (5) classifiers.
A. Common Hardware Processing
Each of four transducer quadrants are independently processed for signal conditioning, signal gain, and
analog-digital conversion. Each of the four hardware channels have identical configurations for the initial
common signal processing.
1) Signal Conditioning: A fixed level of preamplification is applied to the incoming raw analog signal
from each quadrant. A band pass filter passes only frequencies within the broadband range andattenuates frequencies outside that range.
2) Signal Gain: A programmable gain amplifier further amplifies the analog signal with 16 discrete
levels is controlled by an FPGA that receives commands from the SBC through RS-232.
3) Analog-Digital Conversion: 4-channel simultaneous data acquisition is attained at high speed (500 kHz)
and high sampling resolution (16 bits/sample).
B. Channel Mixer
Four individual discrete time series are fed into the channel mixer to produce one composite signal and
four split-beam signals.
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For the broadband processing, the composite signal is formed by the sum of the four discrete time series
from each of the four quadrants. This composite signal is used mostly for broadband signal processing
chain, although it can be also used for target strength calculations to help more accurate split-beam
detection module.
For narrowband processing, the sum of quadrants 1 and 2 are combined to represent upper half of the
transducer, the sum of quadrants 2 and 3 are combined to represent left half of the transducer, the sum
of quadrants 3 and 4 are combined to represent lower half of the transducer, and the sum of quadrants 4
and 1 are combined to represent right half of the transducer, respectively.
C. Narrowband Split-Beam Processing
Narrowband split-beam data processing begins with phase comparison between upper and lower halves
for vertical offset and between left and right halves for horizontal offset. Once the phase differentials
are attained, a decimated version of the split-beam data and broadband target strength data are used for
split-beam detection to provide target tracks for tracking and track classification.
1) Phase Computation: Four discrete time series representing each half of the transducer configuration
go through carrier removal, and then relative phase differences of the two opposite halves are
calculated to provide horizontal phase offset and the vertical phase offset. Also, this data will be
decimated from the original sample rate to a lower, more manageable, data rate before it is exported.2) Phase-Angle Conversion: Two phase differentials are converted into Cartesian angles using the
physical configuration of the transducer.
3) Split-Beam Detection: Broadband target strength calculations and narrowband phase/angle calcula-
tions are used to build a robust target detector. A threshold value of the target strength and variance
of the phase/angle differentials are the primary parameters used target detection. Also, a list of
locations of individual targets are provided for tracking.
4) Tracking: An - tracking system is used to track detected targets [3]. Track tuning to determine theappropriate number of missed detections, association windows, and track initiation and termination
criteria will be conducted during data analysis. Also, more complicated tracking algorithms such as
joint probability data association (JPDA) and multi-hypothesis tracker (MHT) can be applied when
the complexity of the tracking increases [4].
D. Broadband Single-Beam Processing
Broadband single-beam processing starts with the composite signal from all four quadrants. Transmis-
sion loss over the data collection range is compensated by time-varying gain, pulse compression provides
excellent range resolution, and the spectral estimation offers broad spectral features for classification.
1) Target Strength: The composite broadband signal is used to calculate target strength. Unlike the
narrowband target strength with frequency dependency, broadband target strength can provide more
reliable estimation by aggregating broadband response. Accurate target strength helps split-beam
detection module in split-beam processing chain.
2) Time-Varying Gain: The transmission loss is computed from a combination of spherical spreadingloss (20log R) and absorption (R), where R is the range [m] and is the attenuation coefficient[dB/m]. The application of the transmission loss results in a range-compensated discrete time seriesthat is propagated through the remainder of the system.
3) Pulse Compression: A matched filter is used to produce the compressed pulse, which provides im-
proved spatial resolution for signals when a sufficiently large bandwidth is available. Mathematically,
the matched filter is computed by taking the inverse Fourier transform of the product of the Fourier
transform of the transmit signal with the complex conjugate of Fourier transform of the received
signal as described below.
xMF = F1{F(xTX) F
(xRX)} (1)
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where xMF is a compressed pulse time series, xTX is a transmit time series, xRX is a received timeseries, F() is Fourier transform.The effective pulse width of the compressed pulse, TMF, becomes reduced by a factor of the time(T)-bandwidth(B) product. For example, a pulse duration of 667 s is compressed to 16.7 s for the asonar with 60 kHz of bandwidth.
TMF = T 1
T B=
1
B(2)
A few detection criteria including peak threshold, peak window size, and peak separation are applied
to the pulse-compressed data series, producing a list of locations (in range) of the start of each
detected target. A peak detector is used on the detection signal to locate echoes in the return
time series. The peak detector is the technique which first make an estimate about the background
signal level in that neighborhood (e.g., by taking an average around the point) and then comparing
the sample in the center of the neighborhood with the background signal estimate. When a peak
is located, it signifies that a portion of the return has matched up with the filter and therefore
represents a likely reflector in the water. If there is significant separation between last detected echo
and current detection, then a detection is declared and echo extraction occurs. These three sonar
configuration echo detection settings, as well as target strength, are used as coefficients for matched
filter detection process.
4) Spectral Estimation: Spectral estimation is performed using the Fast Fourier Transform (FFT). For
an FFT of size N, the number of frequency bins is N/2. Frequency bin width is the bandwidthof each of the contiguous bins across the usable bandwidth. Frequency bin width is determined by
the size of the FFT and the sample rate. For the classification purposes, the derived echo spectrum
must undergo normalization to accommodate differences in frequency response between different
transducers. This normalization process allows the classifier to be transducer independent
5) Beam Compensation: Uneven broadband beam patterns and sensitivity can be adjusted by compen-
sating for differences in the beam at various frequencies depending on the targets location relative
to the maximum response axis. Details of this beam compensation process are discussed in section
V.
E. Classifiers
Three individual classifiers (track, size, spectral) make independent decisions based on features derived
from track, size, and spectrum. Neural networks are trained using known signals to build each classifier,
and then tested with unseen patterns to evaluate the performance of each classifier. Afterward, the final
decision is made from the fusion classifier for target identification. Alternatively, in place of the fusion
classifier, decision level inference using fuzzy logic or other artificial/computational intelligence algorithms
can be implemented.
1) Track Classifier: The kinematic features such as location, velocity, and movement are extracted
from the - tracker and used to identify the class of underwater target. Also, statistical acousticfeatures such as target strength and spectral/temporal information from individual detections along
each track are added in the pool of features for target identification.2) Size Classifier: The samples associated with a detected target will be used to estimate the size of a
target. Targets that have elongated detection envelopes represent larger targets. As an example, large
aggregations of closely spaced detections are indicative of a marine mammal and sparse detections
of only a few detections within a range window are indicative of fish or other small targets.
3) Spectral Classifier: The spectra produced from the spectral estimation process are used as features for
classification. The classifier is iteratively improved using data from a variety of target assemblages.
The classifier that is utilized is similar to the Parzen estimator that produces probability densityfunctions for each class. Classes will depend on the quality of the ground-truth data. The more
diverse and varied the data set, the better the performance.
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4) Fusion Classifier/Identification: The classification decisions produced by the track classifier, size
classifier, and spectral classifier are merged to form the fusion classifier for the final target identi-
fication.
V. BEA M COMPENSATION
One of the synergistic effects of interleaving narrowband and broadband pings is that is allows for
the compensation of the broadband spectrum using the information from the split-beam processing ofprevious ping. In this section, we describe this compensation process and explain how the narrowband
split-beam can help broadband spectrum adjustment to fully utilize the dynamic range of the directivity
and sensitivity of the transducer.
Unlike a narrowband-only system with fixed directivity pattern and sensitivity in a fixed frequency, a
broadband transducers directivity pattern and sensitivity are a function of frequency. Fig. 4 shows the
directivity patterns of the broadband transducer that was installed in this system. These directivity patterns
were calibrated for eight separate narrowband waveforms in a controlled environment. Note that the higher
the frequency, the narrower the effective beam angle. Also, when a Liner Frequency Modulated (LFM)
waveform is transmitted, the target response with higher frequency component tends to return lower energy
if the target is slightly off MRA.
Fig. 4. Transducer directivity patterns for 8 different narrowband frequencies from 150 kHz to 200 kHz at 10 kHz intervals.
Split-beam processing provides an accurate bearing angle of a target off MRA. Lets assume the z-axis
is the MRA, the x-axis is the horizontal reference, and the y-axis is the vertical reference. Note that theaxes x and y are not in polar domain but in Cartesian coordinate. When a target is detected along thetarget vector, T, and the split-beam processing provides the horizontal and vertical angular offsets, and, relative to the transducer as illustrated in Fig. 5, we can calculate the bearing angle of T relative toMRA as described below in (3).
= arctan1
tan2 + tan2 (3)
Although the measured directivity patterns are not perfectly symmetrical, especially after first side lobes
outside 8 13 depending on frequencies, we can focus on the main lobes of the directivity patterns
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Fig. 5. Split beam target detection and relationship with transducer geometry. See text for a description of the variables.
on each frequency to construct the directivity pattern compensation factor as a function of frequency and
bearing angle off MRA as illustrated in Fig. 6.
As split-beam processing provides accurate bearing angle of the target off MRA, and the broadband
pulse compression and instantaneous time-frequency representation (ITFR), which is discussed in the
following section, provides precise frequency component of target return, we can estimate how much
deviation of sensitivity we lose by interpolation from calibration points.
Fig. 6. Transducer directivity patterns (3D regression). Using this data and knowing the position of the target relative to MRA, it is then
possible to provide a uniform target response across the entire bandwidth.
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Another nonlinear impact on the broadband spectrum is the transducer sensitivities from transmit voltage
response (TVR) and open circuit voltage response (OCVR). The Figure of Merit (FOM) is the sum of TVR
and OCVR, and is useful in providing a relative gauge of the performance of piezoceramic transducers.
Therefore, the normalized total sensitivity can be calculated by combining TVR and OCVR, and then
take out its maximum value as described in (4), which represents the relative deviation of sensitivity
when the frequency component deviates from the resonance frequency of the transducer. Fig.7 shows the
normalized total transducer sensitivity that was also measured in a controlled environment as a function
of frequency.
SN = F OM F OMmax. (4)
Fig. 7. Normalized transducer sensitivity of high frequency version of SciFish2100-D system. Overall sensitivity (FOM) is normalized at
the center (main resonance) frequency at 165 kHz.
Therefore, we can combine both directivity-related adjustment factor and the sensitivity-related factor
together and build the total beam compensation as a function of frequency and target angle off MRA as
shown in Fig.8. In other words, if we can compensate for the transducer-inherent sensitivities using beam
compensation, we will have more target-subjective frequency distribution instead of sensor-subjectiveversion.
V I. EXTENSION TO MULTI-F REQUENCY SONAR
As a broadband signal provides various perspectives within the specific spectral range, it is sometimes
very useful to convert it into a finite number of narrrowband time series in various frequencies. The
biggest advantage in this processing is to emulate multiple narrowband sonars using a single broadband
sonar, although the increased processing burden for real-time implementation must be considered. This
section explains the process of extending a broadband sonar to its equivalent multiple narrowband sonars.
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Fig. 8. Total beam compensation (3D regression) considering target bearing angle and frequency response. Transducer-inherent non-linearity
is cancelled out by taking out the total beam compensation and the generalized target response can be achieved.
A. Time-Frequency Representation
In general, Fourier Transform converts a temporal signal, x(t), to spectral information as described in(5). This is a very useful tool when analyzing a time series from the spectral perspective, but the temporal
information is lost after the conversion.
Fx(f) =
x(t)ej2ftdt (5)
One of the possible methods to retain the temporal information after the transformation is a Short Time
Fourier Transformation (STFT). As described in (6), the STFS uses a sliding time window, w(t), to retaintemporal information while applying the Fourier Transformation. One of the big drawbacks of the STFT
is that it has a fixed overall resolution, because the window function determines whether there is good
frequency resolution or good time resolution. A wider time window provides better frequency resolution
but poorer time resolution whereas a narrower window gives better time resolution but poorer frequency
resolution.
Sx(t, f) =
x()w(t )ej2fd (6)
When a precise frequency response needs to be achieved instantaneously or at very localized instant
in time, a Time-Frequency Representation (TFR) can be applied. The analytically appealing Cohens
Generalized Time Frequency Representation (GTFR) of a temporal signal, x(t), is shown in (7) whenseeking a performance improvement of the spectrogram and making instantaneous representation of both
temporal and spectral information available.
Cx(t, f) =
(, )x
t
2
x
t +
2
ej2dd (7)
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It is now straightforward to state some of the commonly used constraints imposed on the GTFR and
their corresponding interpretation as kernel constraints using the kernels listed in (8).
(, ) =
1, W igner V illee(2)
2/, Choi Williamsej, Kirkwood Rihaczekej2||, P agecos(), M argenau Hillsinc(), Born Jordong()||sinc(2a), Z hao Atlas Marks
(8)
where , a, and g() are kernel parameters selected to best suit the application.
B. Real-Time Implementation of Instantaneous Time Frequency Representation (ITFR) for Multi-Frequency
Sonar
Performance of the GTFR depends on the choice of kernel in different applications. Those kernels
listed above are well known to reduce interference (cross-term) during the conversion. However, complex
kernels reduce also the feasibility of implementing the algorithm performing in real-time, especially when
a high ping rate is required to monitor migratory fish schools in riverine environment. After reviewing each
of those algorithms, the Wigner-Ville Distribution (WVD) was best determined to be the most practical
choice for instantaneous time-frequency representation.
The WVD, shown in (9) can be seen as a special case of GTFR, Cx(t, f), when the kernel, (, ), is 1.Although there is the potential not to be able to remove the interference without a more effective kernel,
computational complexity of Wigner-Ville distribution is much lower than those of the listed kernels
with temporal or/and spectral constraints. When considering these trade-offs, the benefit for real-time
implementation with reasonable computational burden overshadows the aforementioned drawbacks.
W Vx(t, f) =
x
t
2
x
t +
2
ej2fd (9)
An example of field data collected in Ship Creek, Anchorage, AK, on August 12, 2004 illustrates the
ITFR process with WVD. Fig. 9 shows a snapshot of a ping recorded in a single composite broadband
channel at sampling frequency of 500 kHz. Because of the protection circuit in hardware electronics,
all signals were clipped at 1 V. Thus, the actual LFM transmit waveform from 135 kHz to 195 kHzwith amplitude of 100 Vpp appears at 0667 sec, followed by a relatively low transducer ringing until840 sec, but they appear clipped in the collected time series. Meanwhile, a clear target return of aChinook Salmon (Oncorhynchus tshawytscha) shows up between 3.72 msec and 4.40 msec.
When we focus on the target return of the time series in Fig. 9, a power spectrum of the target can
be calculated using the discrete version of (5) with FFT size of 512. In this calculation, all discrete
samples between 3.72 msec and 4.40 msec were included to produce the target power spectrum shown in
Fig. 10, which coincides with the frequency range of the transmit waveform. The shape of the distribution
within the broadband frequency range between 135 kHz and 195 kHz can be used as features for target
identification in spectral classifier.
One of the properties of a spectrogram produced from a STFT in (6) is the ability to retain the temporal
information while providing spectral features. Fig. 11 shows the spectrogram of a whole time series using
an FFT size of 128 and time window size of 125 samples (=250 msec). The spectral features show up
along the time axis except both edges of the time series caused by time window, but smearing features
dilute the resolution in both time and frequency axes.
To retain finer frequency resolution at every time interval with realistic computational expense, the
WVD ITFR is used as shown in Fig. 12. The spectral features show up along the time axis at every
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0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
x 103
1.5
1
0.5
0
0.5
1
1.5Received Time Series (Composite Broadband Signal)
Time [second]
SignalLevel[V]
Fig. 9. An example of field data collected in Ship Creek, Anchorage, AK, on August 12, 2004. LFM from 135 kHz to 195 kHz for 667sec
pulse length was transmitted and is seen at the beginning of the time series. A clipped version of transmit waveform that appears between 0
and 667sec was recorded, although the actual transmit signal level was approximately 100 Vpp. Transducer ringing followed from 667 sec
and faded away at about 840sec. The target return from a King Salmon shows up clearly between 3.72 msec and 4.40 msec.
instance without black-out sections on both edges, and much sharper resolution was achieved in both time
and frequency axes.
A spectrum is available at every time interval and extracting an horizontal strip at a certain frequency
from Fig. 12 represents a narrowband-equivalent time series.
Besides, to attain spectral energy at a fixed frequency, an envelope of each time series needs to be
calculated by shifting the spectrum by the constant frequency offset, fo. The relationship between timedomain and frequency domain in frequency shift is denoted in the equation below.
e2f0tx(t) Fx(ej2(ff0)) (10)
Thus, multi-channel time series from a single broadband signal can be derived to produce multi-
frequency narrowband signals. Fig. 13 focuses on the time segment between 3.72 msec and 4.40 msec
to show 16 narrowband time series extracted from a single broadband signal. In this example, narrowband-
equivalent target returns are in frequencies 135.0 kHz, 138.2 kHz, 144.7 kHz, 147.9 kHz, 151.1 kHz, 154.4 kHz
160.8 kHz, 164.0 kHz, 167.3 kHz, 173.7 kHz, 176.9 kHz, 180.2 kHz, 183.4 kHz, 189.8 kHz, 193.1 kHz, and
196.3 kHz.
Although some artifacts with negative energy are present after computation (a well-known side-effect
of the WVD), which is not physically possible, some simple methods such as taking the absolute values
or discarding negative values can easily cure these processing anomalies.
VII. EXPERIMENTAL RESULTS
There have been several field tests of the combined broadband/split-beam sonar system, including
controlled experiments in pools and a lake and deployments along a river for the duration of a large
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0 50 100 150 200 25040
30
20
10
0
10
20
30Target Power Spectrum
Frequency [kHz]
Power[dB]
Fig. 10. Power spectrum from target detection. All samples in the time window from 3.72 msec to 4.40 msec were extracted and the power
spectrum was calculated to provide the distribution of energy in frequency domain. Most of the energy is concentrate within 135 kHz195kHz
according to the transmit waveform of LFM.
Fig. 11. Spectrogram with FFT size of 128 and time window size of 125 samples (=250 msec). The spectral features along the time axis
are available, but the resolutions in time and frequency depend on the FFT size and time window size. Also, spectral features on both ends
of time series are not available due to the use of time window.
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Fig. 12. Wigner-Ville distribution for instantaneous time frequency representation. Improvement of resolution is noticeable in both time
and frequency domains compared with STFT.
Fig. 13. Sixteen individual time series are extracted from a broadband signal using ITFR with Wigner-Ville distribution. Each of the signals
is equivalent to its narrowband time series at each of those 16 constant frequencies. Because the frequency of transmit time series (LFM) is
linearly increasing in time, the lower frequency components arrive earlier than those of higher frequency components in the returned target
echo.
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salmon run. All of the experiments were conducted in Alaska, with pool tests being performed during
the winter and lake and river testing during the summer. These experiments were conducted to verify that
the combined sonar operates correctly in their individual split-beam and broadband modalities. In a later
paper, we will present results of the beam compensated broadband performance in a series of controlled
classification studies [36].
Three tests are described in the sections that follow. The first of these tests focused on the ability of
the split-beam sonar system to track and enumerate fish during high passage rates. The remaining two
tests focused on the ability of the broadband sonar system to perform target identification. The first of
these broadband tests was conducted in a lake focused on the ability to discriminate a swimmer from
similar targets [35]. The second broadband test was conducted with Pacific Northwest National Lab for
discriminating smolt-size Chinook salmon from aquatic macrophytes [34].
A. Splitbeam Experiment
The primary purpose of the field test in Wood River, Alaska, was to evaluate narrowband split-beam
sonar processing. Wood River is known to have a large Sockeye Salmon (Oncorhynchus Nerka) return
between late June and early July. The Alaska Department of Fish and Games (ADF&G) monitors this
migration from several counting towers along the rivers shore. The tower crews visually count individual
fish passing underneath the tower for 10 minutes every hour during the period of fish run when the majorityof the fish are migrating upriver and then estimates the hourly fish passage rate from the subsample.A combined broadband/split-beam sonar system was deployed in the Wood River near Aleknagik,
Alaska, fully operating with minimum interruption for power and data backup from 6/26/06 to 7/4/06.
Fig. 14 shows the combined sonar system deployed next to an ADF&G counting tower.
Fig. 14. ADF&G counting tower and combined sonar system deployed to monitor Sockeye salmon migration in Wood river, AK.
Throughout the test period, the sonar system was continuously transmitting 200 sec narrowband pulsesat 15 pings per second. Periodically, a tower count was made when the visibility in the river was adequate.
The tower counts and the sonar counts were performed independently and the results were provided to a
third party (Dr. Tim Mulligan). The comparative results were not reported until after the summer season
was completed and Dr. Mulligan had sufficient time to perform his comparative evaluation.
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The comparative evaluation was conducted using all visual tower counts. Each of the corresponding
sections of recorded narrowband signals were extracted. Utilizing the EchoView application from Sonar-
Data (now Myriax, www.echoview.com), the split-beam data was processed offline to count fish. The
number of fish tracks in each ten-minute tower count session were logged and compared with that of
visual tower count. Table I enlists date, start time, end time, visual fish count from counting tower, and
fish track count from off-line sonar data analysis of each of the 20 sessions where tower counts were
taken.
TABLE I
SPLIT-BEAM SONAR FIS H COUNT COMPARISON BETWEEN VISUAL TOWER COUNT AND SONAR SYSTEM TRACK COUNT
Date Start Time End Time Tower Count Sonar Count
6/29/06 09:21 09:31 441 430
6/29/06 10:00 10:10 165 156
6/29/06 11:11 11:21 868 654
6/29/06 11:55 12:05 467 426
6/29/06 14:03 14:13 373 348
6/29/06 14:57 15:07 770 584
6/30/06 00:02 00:12 0 6
6/30/06 06:30 06:40 918 647
6/30/06 15:24 15:34 505 491
6/30/06 15:41 15:51 217 215
6/30/06 18:40 18:50 430 397
6/30/06 18:50 19:00 590 499
6/30/06 19:00 19:10 651 478
7/1/06 06:23 06:33 1284 902
7/1/06 23:57 00:07 1074 839
7/2/06 23:11 23:21 1023 744
7/2/06 06:56 07:06 1210 923
7/3/06 06:51 07:01 953 771
7/3/06 11:23 11:33 330 304
7/3/06 14:59 15:09 1710 939
For the analytical comparison of each fish counting technique, each fish count was converted into an
hourly passage rate and directly compared as shown in Fig. 15. The horizontal axis represent the visual
fish count from counting tower, the vertical axis represents the number of fish tracks extracted from
sonar data recorded at the same time, and the dotted diagonal line represents a virtual 1:1 mapping as a
reference, respectively.
Also, the 2nd order polynomial curve fitting, p(x), that is described in (11) can be derived from the 20data points and depicted in a solid curve in Fig. 15.
p(x) = 0 + 1x + 2x2 + 3x
3 (11)
where 0=-2.5267x109, 1=-4.0365x10
6, 2=0.8510, 3=160.8368, and x is a vector including 20 sonarcounts.
Of the nine sessions where the hourly fish passage rate was under 3000 fish per hour, the sonar counts
and tower counts closely agreed with a very small margin. Deviations are seen in the remaining sessions
as the fish passage rate increased. There are two possible reasons for the sonar under-counting when the
population increased drastically. One possible cause is the fish swimming out of sonar beam angle so that
the system could not sufficiently ensonify those fish. The other cause could be that many fish would have
traveled too close to each other to make multiple tracks appear to be a single track in the recorded data
although they swam inside the beam angle.
Considering the majority of the Salmon migrations were reported with less than 3000 hourly fish passage
rate, the split-beam performance evaluation in Wood Rover was considered successful.
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Fig. 15. Fish count comparison for hourly passage rate. The square data points represent the relationship between the visual fish count
from counting tower (horizontal axis) and the number of fish tracks extracted from sonar data (vertical axis) along the dotted diagonal line
for 1:1 mapping as a reference. The 3rd order polynomial fitting curve shows the trend of the performance (solid line).
B. Broadband Experiment - Swimmer Identification
A field test has been conducted to demonstrate the capability of the broadband sonar system to identify
swimmers from other underwater objects. Classifiers were built and tested from data collected from an
swimmer donned with mask, fins, and a scuba tank in Marion Lake, Alaska in 2005. The test results using
low frequency version of the combined sonar system (60 kHz120 kHz) are described in the followingsubsections.
Two known targets (air filled 2-liter plastic bottles) were placed in the middle of water column as
reference targets emulating the approximate size and shape of human lungs. One bottle was placed at
30 m and the other was placed at 71 m range. A swimmer swam in approximately a straight line along the
MRA from a location 22 m from the transducer toward the first bottle at 30 m. After passing the first bottle,the swimmer came to the surface to adjust his swimming direction, and then swam in approximately a
straight line toward the second bottle. The swimmer again surfaced to re-adjust the swimming direction
at 65 m, and swam past the 2nd bottle up to 95 m range where he turned around and attempted to retrace
the same route back. The swimmer surfaced several times to re-adjust his direction as he was coming
back.
Throughout the swimming sequence there is a significant number of air bubbles produced from the scuba
gear and the swimming motion, especially when the swimmer was at close range and at the surface. The
presence of these bubble clouds had the effect of diminishing the separation between the swimmer and
the air-filled bottles for the classifier, as the air bubbles were necessarily included with the swimmer and
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represented a significant portion of the backscatter from the swimmer.
Only a small portion of the collected data were extracted for spectral comparison using principal
component analysis (PCA). Approximately 200 echoes from the second bottle at 71 m were compared with
those of the swimmer at 6370 m range outward bound (flippers toward the sonar, generating bubbles).The extracted spectra within the frequency of interest (60 kHz120 kHz) were converted into principalcomponents.
Fig. 16 shows the two largest principal components that provide the greatest separation between two
target types (swimmer and bottle). The use of PCA is very useful as an intial analysis tool as it provides
a preliminary indication of separability among different targets in high dimensional feature space. The
PCA test was very promising, with only two principal components out of 63 distinct spectral energy bins
indicating the possibility of separation among different target classes.
Fig. 16. Principal component analysis: 2-dimensional presentation of 2 different targets. Two largest principal components that provide
the greatest separation between two target types (swimmer and bottle) were plotted as a preliminary observation of separability. There are
several points that appear to be overlapped, although two noticeable clusters can be distinguished.
Since some degree of separation was observed between the swimmer and the bottle data, we anticipate
that the artificial neural network would improve the discrimination between the two types, because (1)
the artificial neural network utilizes the full degree of freedom of feature sets without data loss, and (2)
the artificial neural network provides flexible non-linear separation.
Having used PCA to demonstrate that separability can be achieved, an artificial neural network was
trained to build a classifier. In this example, a multi-layered perceptrons was trained using 10% of
the randomly extracted spectra from two different data sets: swimmer target and non-swimmer target,
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respectively. The swimmer data set was collected when the swimmer was on the surface of the water
for adjusting the swimming direction and the bottle data set was collected when the swimmer was not
present.
Fig. 17. Classification result for swimmer vs. bottles at Marion lake, AK. The swimmer track stands out with air bubbles that were caused
by the swimming (kicking) motion. Two tracks of bottles at about 30 m and 70 m were classified differently from that of swimmer.
The trained neural network was used to colorize target detections by class. Fig. 17 shows the final
classification result. The black detections represent the swimmer and the gray detections represent thebottle. Although there are several intermittent misclassifications, the overall track of the swimmer appears
obvious as well as that of the bottle. In fact, this plot does not simply show the testing of the classification,
but includes both training and testing. However, using only a small fraction of the entire data set provided
very good generalization capability.
C. Broadband Experiment - Smolt Fish Identification
The Pacific Northwest National Laboratory conducted a study of the broadband fisheries sonar systems
ability to discriminate between Chinook Salmon smolt and aquatic macrophytes [34]. Like the counting
study that was performed with the split-beam evaluation, a blind study was conducted in which a trusted
third party (R. Johnson) held back the identity of the test subjects until after the system reported its results.Data collection was conducted at one of the rectangular ponds in 100K-basin area at the Hanford
Site near Richland, WA, from August 31 through September 2, 2004. All data collection work was
conducted with a horizontal acoustic projection across the narrow axis of the pond with the anticipation
of discriminating Chinook salmon smolt, in dorsal or ventral aspect from various assemblages of aquatic
macrophytes.
The targets were located at 16.7 m from the transducer, and the back-wall was 1.7m beyond the target
(18.4 m from the transducer). The water depth of the pond was approximately 5.1m, and the transducer
and the acoustic targets were deployed in the middle of the water column approximately at 2.55 m from
the bottom of the pond as illustrated in Fig 18.
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Fig. 18. Geometry of the pond that field test was conducted in the Hanford Site near Richland, WA. Dimensions of the pond and the
relative distance between target and transducer are illustrated.
Juvenile fall Chinook salmon 130-150 mm in length were transported to the site live in a large aerated
cooler. Just prior to being tested fish were anesthetized using MS 222. All fish were tested in good to
excellent condition (i.e. minimal scale and fin loss). Macrophytes were collected at a marina in North
Richland and consisted of water milfoil and elodea.
A special tool was devised to control the aspect of targets. A long bar was tied across the top of the
hand railings on either side of a walkway and another bar attached at one end of the long bar was rotated
by a rope to change the aspect angle of the target. The fish and macrophytes were positioned in the centerportion of the sonar beam along the MRA. To suspend the fish, clear monofilament line was fastened to
the bar and the fish was fastened to the line by threading line down one side of the mid portion of the
fish. A lead weight was added approximately 4 ft below the fish to keep the line tight. To achieve the
proper fish orientation a separate section of monofilament was attached the pivoting arm 4 ft from the fish
line. The line was attached to mouth region and weighted at the lower end. By moving the pivoting arm
the fish aspect could be changed. For all tests on either a dorsal or ventral aspect was used. Macrophytes
were deployed by using a separate weighted line and attached using a small section of monofiliament to
a loop in the line.
The transducer was mounted at the end of a metal tube, which was attached to a wood block tied to
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the bottom rails of the path to keep the transducer in the middle of the water column firmly pointing
straight to the target under the changing weather condition.
The dry end side of the combined sonar system, incuding a PC, monitor, and terminal box, were set
up in a rented moving truck, which was about 50 m away from the wet-end deployment. 100 m-long
SciFish2100 transducer cable connected between the transducer and the terminal box.
The first 1.5 days were used for the data collection and the creation of the neural network classifier.
The remainder of the time was used to collect blind test sets. In total, 8 different fish and 7 different
macrophytes were used to collect training data. Also, 86 unknown targets were used to collect blind test
datasets, whose identification would be released after the test results were provided.
All 4,257 pings of the collected data were fed to train a neural network. Of the classifier samples,
2419 samples are actual fish and the remaining 1838 samples are actual macrophytes. The classifier made
probabilistic decision in terms of the degree of similarity between 0 and 1.0. If the value is close to 0,
the sample is fish, and if it is close to 1.0, it is macrophyte. However a hard (deterministic) decision can
also be made by applying a threshold value.
A total of 86 blind test sets were collected during the 2nd and 3rd day, but the identifications of 5
blind test sets (ID 20911, 20919, 21110, 21125, 21134) were released later and used for training the
neural network classifier. Thus, those five data sets were not considered as true blind test data sets and
they were not counted for testing. Since all blind test data sets were collected under in a more controlled
environment and under slightly different weather conditions than the training data sets, adding those sets
would make training data sets more representative of a varying realistic environment.
A total of 81 unknown and 5 known data sets were tested to evaluate the performance of the classifier
built by the known data sets. Table II shows the summary of the blind test results. The columns under
Classified as were outputs from the neural network classifier. Each decision with higher probability
above 50% was declared as corresponding class. The columns under Reality (Ground Truth) were
actual truth table released after we had provided the outputs of classifier to Pacific Northwest National
Lab. The column Result tells if the classifier made correct decision or not.
TABLE II. CLASSIFICATION RESULT FROM BLIND TESTS BASED ON THE CLASSIFIER BUILT USING THE KNOWN SAMPLES.
Order Target ID PingsClassified as Reality (Ground Truth)
ResultFish Macrophyte
Count % Count % Decision Actual Aspect
1 11451 251 239 95.21 12 4.78 Fish Fish dorsal TRUE
2 11456 161 81 50.31 80 49.68 Fish Fish dorsal TRUE
3 11513 260 184 70.76 76 29.23 Fish Fish ventral TRUE
4 11522 260 16 5.38 281 94.61 Macrophyte Macrophyte small/short TRUE
5 11531 211 14 6.63 197 93.36 Macrophyte Macrophyte large/short TRUE
6 11541 257 189 73.54 68 26.45 Fish Fish dorsal TRUE
7 11548 248 19 7.66 229 92.33 Macrophyte Macrophyte long/skinny TRUE
8 11554 137 125 91.24 12 8.75 Fish Fish dorsal TRUE
9 11604 238 90 37.81 148 62.19 Macrophyte Macrophyte no entry TRUE
10 11609 257 113 43.96 144 56.03 Macrophyte Fish dorsal FALSE
11 11616 141 132 93.61 9 6.38 Fish Fish ventral TRUE
12 11630 277 78 28.15 199 71.84 Macrophyte Macrophyte very small TRUE
13 11642 233 211 90.55 22 9.44 Fish Fish dorsal TRUE
14 11648 262 213 81.29 49 18.70 Fish Fish ventral TRUE
15 11705 175 170 97.14 5 2.85 Fish Fish ventral TRUE
16 11715 260 223 85.76 37 14.23 Fish Fish dorsal TRUE
17 11723 279 249 89.24 30 10.75 Fish Fish dorsal TRUE
18 11735 277 172 62.09 105 37.90 Fish Fish dorsal TRUE
19 11741 279 263 94.26 16 5.73 Fish Fish ventral TRUE
20 11746 219 2 0.91 217 99.08 Macrophyte Macrophyte small millfoil TRUE
21 20829 223 107 47.98 116 52.01 Macrophyte Fish dorsal FALSE
continued on next page
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continued from previous page
Order Target ID Pings
Classified as Reality (Ground Truth)
ResultFish Macrophyte
Count % Count % Decision Actual Aspect
22 20835 268 1 0.37 267 99.62 Macrophyte Macrophyte small poto TRUE
23 20851 262 257 98.09 5 1.90 Fish Fish dorsal TRUE
24 20857 276 111 40.21 165 59.78 Macrophyte Macrophyte long strand TRUE
25 20906 292 0 0 292 100 Macrophyte Macrophyte short millfoil TRUE
26 20911 136 136 100 0 0 Fish Fish dorsal TRUE
27 20919 121 121 100 0 0 Fish Fish ventral TRUE
28 20933 251 243 96.81 8 3.18 Fish Fish dorsal TRUE
29 20939 159 22 13.83 137 86.16 Macrophyte Macrophyte no entry TRUE
30 20945 268 158 58.95 110 41.04 Fish Macrophyte no entry FALSE
31 20949 265 177 66.79 88 33.20 Fish Fish dorsal TRUE
32 20953 218 214 98.16 4 1.83 Fish Fish dorsal TRUE
33 20958 147 146 99.31 1 0.68 Fish Fish dorsal TRUE
34 21005 259 223 86.10 36 13.89 Fish Fish ventral TRUE
35 21009 207 179 86.47 28 13.52 Fish Fish dorsal TRUE
36 21014 198 57 28.78 141 71.21 Macrophyte Macrophyte small TRUE
37 21020 186 4 2.15 182 97.84 Macrophyte Macrophyte small TRUE
38 21026 187 41 21.92 146 78.07 Macrophyte Macrophyte small/longer TRUE
39 21033 230 22 9.56 208 90.43 Macrophyte Macrophyte small millfoil TRUE
40 21039 154 140 90.90 14 9.09 Fish Macrophyte very small poto FALSE
41 21046 165 55 33.33 110 66.66 Macrophyte Macrophyte small TRUE
42 21050 232 226 97.41 6 2.58 Fish Fish dorsal TRUE
43 21055 219 90 41.09 129 58.90 Macrophyte Macrophyte no entry TRUE
44 21059 218 195 89.44 23 10.55 Fish Fish ventral TRUE
45 21103 210 184 87.61 26 12.38 Fish Macrophyte small poto FALSE
46 21110 123 0 0 123 100 Macrophyte Macrophyte med. Millfoil TRUE
47 21116 233 203 87.12 30 12.87 Fish Fish dorsal TRUE
48 21120 242 98 40.49 144 59.50 Macrophyte Fish ventral FALSE
49 21125 239 0 0 239 100 Macrophyte Macrophyte large poto TRUE
50 21129 228 221 96.92 7 3.07 Fish Fish dorsal TRUE
51 21134 206 0 0 206 100 Macrophyte Macrophyte small poto TRUE
52 21141 223 26 11.65 197 88.34 Macrophyte Macrophyte small poto TRUE
53 21147 143 3 2.09 140 97.90 Macrophyte Macrophyte small millfoil TRUE
54 2115 200 169 84.50 31 15.50 Fish Macrophyte small millfoil FALSE
55 21158 186 17 9.13 169 90.86 Macrophyte Macrophyte small millfoil & poto TRUE
56 21202 220 8 3.63 212 96.36 Macrophyte Macrophyte bigger bundle poto TRUE
57 21209 205 69 33.65 136 66.34 Macrophyte Macrophyte poto TRUE
58 21214 210 157 74.76 53 25.23 Fish Fish ventral TRUE
59 21257 235 39 16.59 196 83.40 Macrophyte Macrophyte small millfoil TRUE
60 21302 191 178 93.19 13 6.80 Fish Fish dorsal TRUE
61 21309 207 43 20.77 164 79.22 Macrophyte Macrophyte med poto TRUE
62 21314 157 41 26.11 116 73.88 Macrophyte Macrophyte med poto TRUE
63 21319 196 126 64.28 70 35.71 Fish Macrophyte smaller millfoil FALSE
64 21324 233 30 12.87 203 87.12 Macrophyte Macrophyte long poto TRUE
65 21328 246 200 81.30 46 18.69 Fish Fish dorsal TRUE
66 21333 228 15 6.57 213 93.42 Macrophyte Macrophyte long poto TRUE
67 21338 209 94 44.97 115 55.02 Macrophyte Macrophyte small milllfoil TRUE
68 21344 239 88 36.82 151 63.17 Macrophyte Macrophyte small millfoil TRUE
69 21351 254 208 81.88 46 18.11 Fish Fish ventral TRUE70 21355 215 206 95.8 9 4.18 Fish Fish dorsal TRUE
71 21359 211 91 43.12 120 56.87 Macrophyte Macrophyte small millfoil TRUE
72 21404 247 221 89.47 26 10.52 Fish Fish ventral TRUE
73 21410 258 122 47.28 136 52.71 Macrophyte Fish dorsal FALSE
74 21413 249 234 93.97 15 6.02 Fish Fish ventral TRUE
75 21417 241 94 39.00 147 60.99 Macrophyte Macrophyte small millfoil TRUE
76 21422 192 114 59.37 78 40.62 Fish Macrophyte small poto FALSE
77 21427 130 103 79.23 27 20.76 Fish Fish ventral TRUE
78 21431 213 118 55.39 95 44.60 Fish Fish dorsal TRUE
79 21436 182 157 86.26 25 13.73 Fish Macrophyte small poto FALSE
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continued from previous page
Order Target ID Pings
Classified as Reality (Ground Truth)
ResultFish Macrophyte
Count % Count % Decision Actual Aspect
80 21443 230 59 25.65 171 74.34 Macrophyte Macrophyte long strand poto TRUE
81 21451 264 234 88.63 30 11.36 Fish Fish ventral TRUE
82 21501 210 103 49.04 107 50.95 Macrophyte Macrophyte med. poto TRUE
83 21511 243 209 86.00 34 13.99 Fish Fish ventral TRUE
84 21522 170 54 31.76 116 68.23 Macrophyte Macrophyte med. poto TRUE
85 21528 253 102 40.31 151 59.68 Macrophyte Macrophyte long strand poto TRUE
86 21532 210 236 97.92 5 2.07 Fish Fish dorsal TRUE
Excluding the five known blind test sets, the results showed that 70 true decisions and 11 false
decisions, resulting in (86.42% correct decision) The system had the most difficulty distinguishing fish
from macrophytes when smaller portions of macrophytes were used. The confidence levels were near 50%
in the majority of the cases.During 3-day data collection period, we had rough weather condition, especially, on 2nd and 3rd day
due to strong wind. One of the possible reasons of the incorrect classification could be that the relative
target-transducer geometry change often pushed the target out of the beam angle causing weak signal
return and intermittent detections. In such a case the returned signals usually dont represent the whole
target.Overall performance of broadband fish identification was satisfactory in discriminating smolt-size
Columbia river Chinook salmon against aquatic macrophytes.
VIII. CONCLUSION
This paper has presented a recently developed sonar system that combines a broadband single-beam and
narrowband split-beam into a single sonar system. By combining these two sonar modalities, it provides
a research tool that can provide target tracking and target identification a capability that is not available
in either system independently.Furthermore, synergistic impacts from both modalities provides exceptional performance. We discussed
details of broadband spectrum adjustment using beam compensation from split beam bearing angle and
sensitivity of transducer. Also we used instantaneous time frequency representation to create variouspseudo-narrowband signals for multi-frequency data streams.
Field evaluations have been conducted in Alaskan lake and rivers to verify that each portion of the
system narrowband and broadband provided the ability to track and identify targets.Efforts are currently underway to validate and automate the beam-compensation capability provided by
the split-beam processing and improve the real-time performance of the multi-frequency portion of the
sonar.
APPENDIX A
TECHNICAL SPECIFICATION OF COMBINED SPLIT-B EA M / BROADBAND SONAR SYSTEM
Technical specification of high frequency version (135 kHz to 195 kHz) of the combined split-beam
narrowband / single-beam broadband sonar system is described below. the low-frequency version sharesmost of the technical specification except the frequency range (60 kHz to 120 kHz). Fig. 19 provides a
picture of the sonar system.
ACKNOWLEDGMENT
The authors would like to thank Alaska Department of Fish and Games (ADF&G) and Alaska Sus-
tainable Salmon Fund (AKSSF) for their supports and thoughtful reviews during several rounds of
development. They would also like to thank Donald Degan and Anna-Maria Mueller from Aquacoustics,
Inc. and Timothy Mulligan for their valuable comments and reviews. Further, they thank the reviewers
of this paper for very insightful comments and suggestions, which they believe have led to a remarkable
improvement in quality and presentation of this paper.
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TABLE III
TECHNICAL SPECIFICATION OF HIG H FREQUENCY VERSION OF THE COMBINED SPLIT-B EA M/B ROADBAND SYSTEM
Transmit Specification
Beam Shape/Width 6 degrees conical at 165kHz
Frequency, Broadband 135kHz to 195 kHz
Frequency, Split-Beam 16510 kHz
Frequency, Multi-Freq. Any non-overlapping 5 kHz bands between 135 kHz and 195 kHz
Waveform, Split-Beam & Multi-Freq. Continuous Wave (CW)Source Level 214 dB at 165 kHz
Detection Range (min/max) 12 to 200 m at 165 kHz
Range Resolution, Broadband 1.2 Cm
Power Settings 16, from 0 (listen only) to 15 (max)
Pulse Length, Broadband & Multi-Freq. 15.40 sec (compressed)
Pulse Length, Split-Beam 200 sec (adjustable)
Ping Rate (min/max) 1 ping / 2 secs to 30 pings / sec
Receive Specification
Bandwidth, Broadband 60 k Hz
Bandwidth, Split-Beam 10 k Hz
Gain 16 settings (programmable between 20 d B and 60 d B
A/D Conversion 500 Ksamples/sec per channel simultaneously
Split-Beam Angular Resolution 0.1 degrees
Processor SpecificationSingle Board Computer 1.1G Hz Pentium M
A/D & D/A Converter 4 channels, 500 Ksamples/sec
Network TCP/IP Ethernet (10/100/1000 M bps)
Software Specification
Data Storage 4 channels, raw data
Export Formats Echoview (multi-freq, split-beam)
Displays Sonar Grams, Split-Beam
Dimensions Specification
Sonar 22.8 Cm Diam, 53.8 Cm Length
Cable 30 m Length, OD 1.45 Cm
Fig. 19. Combined split-beam narrowband / single-beam broadband sonar system with terminal box and underwater cable.
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Jae-Byung Jung (S01-M02) received the B.S. and M.S. degrees in electronics engineering from Hanyang University,
Korea, in 1993 and 1995 respectively. He was an Assistant Research Engineer at LG Industrial Systems, Co., Ltd.,
Korea, from 1995 to 1996. He earned the Ph.D. in electrical engineering from the University of Washington, Seattle,WA, in 2001. From 2001 to 2002, he was a Post-Doctoral Research Associate at the Department of Electrical
Engineering, the University of Washington. In 2002, he joined Scientific Fishery Systems, Inc., where he is currently
a Senior Engineer responsible for research and development of underwater acoustic instruments.
His areas of interest include neural networks, fuzzy systems, evolutionary algorithms, signal processing, computer
vision, robotics, and software engineering.
Alexander B. Kulinchenko was born in Lviv, Ukraine in 1971. In 1993, Mr. Kulinchenko received Electro-Mechanical
Engineering Degree in State University Lvivska Polytechnika, Lviv, Ukraine. Continuing education Mr. Kulinchenko
received Master of Science Degree in Electro-Mechanical Engineering in State University Lvivska Polytechnika,
Lviv, Ukraine, in 1994. In 1995 Mr. Kulinchenko moved to Detroit, MI where he worked as draft assistant at Lear
Seating Corporation, Plymouth, MI. In 1997, Mr. Kulinchenko moved to Anchorage, AK. In December 2001, Mr.Kulinchenko graduated from University of Alaska in Anchorage with Bachelor of Science Degree in Computer Science.
Since August 2001, Mr. Kulinchenko has been working as software engineer at Scientific Fishery Systems, Inc. Mr.
Kulinchenko has experience in software applications development, modifications and testing. Good knowledge of Java,
Enterprise Java Beans (EJB), Extensible Markup Language (XML), C++, Intel Image Processing Library (IPL), Open
Source Computer Vision Library (Open CV), Visual Basic (VB), MatLab and Neural Networks. Knowledge of Microsoft Visual Developer
Studio (C++, VisualBasic, SourceSafe), Borland JBuilder (Java).
Mr. Kulinchenko has also experience in electro-mechanical engineering including knowledge of AutoCAD.
Patrick K. Simpson received his Bachelor of Arts degree in Computer Science in 1986 from the University of
California at San Diego. Soon after graduating, Mr. Simpson has distinguished himself in the application of neural
networks, fuzzy systems, and artificial intelligence to difficult defense related problems in areas such as electronic
intelligence, radar surveillance, sonar signal identification, and various aspects of automated diagnostics.
Early in his career, Mr. Simpson wrote several archival papers, taught several short courses, wrote a text book
that has been used in college courses around the United States, and lectured on the theory and application of neural
networks and fuzzy systems world wide. Mr. Simpson served as President of the IEEE Neural Networks Council in
1994 and was the General Chairman of the 1998 IEEE World Congress on Computational Intelligence.
In 1992, Mr. Simpson founded Scientific Fishery Systems, Inc. (SciFish) to develop technologies for more efficient
fisheries. Through initial funding from the Small Business Innovative Research (SBIR) program, and subsequent funding from commercial
sales, government sales, and continued research and development, SciFish has developed several technologies that have been applied to
various aspects of the fishing industry.
James W. Tilley received the B.S. degree in electrical engineering from the University of Nevada, Las Vegas in 1998.
Upon graduation, Mr. Tilley moved to the Seattle area and began work in the field of electromagnetic compatibilitytesting. In January of 2005, Mr. Tilley began working for Scientific Fishery Systems on their split-beam sonar system.
Later that year, he transferred to sister company Alaska Native Technologies, LLC. where he is currently working on
unmanned underwater systems.