RADAR Signal Data Acquisition, Conditioning & Processing System

7/31/2019 RADAR Signal Data Acquisition, Conditioning & Processing System

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INNOVATION COMMUNICATIONS SYSTEMS LIMITED

8-3-898/30/2, Nagarjuna Nagar Colony, Ameerpet

Hyderabad 500073, India

Tel: +91-40-23752790/23730083 Fax: +91-40-23752788

Homepage: www.icsglobal.biz Email: [email protected]

RADAR Signal Data Acquisition,

Conditioning & Processing System

TECHNICAL PROPOSAL


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Innovation Communications systems Ltd.

CONTENTS

INTRODUCTION ......................................................................................... 5

FUNCTIONAL FEATURES ............................................................................. 6

FUNCTIONAL BLOCK DIAGRAM .................................................................. 6

FUNCTIONAL BLOCK DESCRIPTION ............................................................ 7

DATA ACQUISITION.........................................................................................................................7

DIGITAL DOWN CONVERSION..............................................................................................................9DECODING ...............................................................................................................................12

COHERENT INTEGRATION..................................................................................................................13

NORMALIZATION .........................................................................................................................13

WINDOWING ..............................................................................................................................13

FOURIER ANALYSIS .......................................................................................................................13

SPECTRAL AVERAGING ...................................................................................................................14

SPECTRUM CLEANING ..................................................................................................................14

NOISE LEVEL ESTIMATION.................................................................................................................15

MOMENTS ESTIMATION...................................................................................................................16

UVW COMPUTATION......................................................................................................................18

SCOPE OF WORK .....................................................................................19SIMULATION DETAILS ............................................................................20

DELIVERABLES ........................................................................................26

DEVELOPMENT SCHEDULE ....................................................................... 26

TESTING & PROVING ................................................................................26

TRAINING AND DOCUMENTATION ............................................................ 26

OTHER REQUIREMENTS: ..........................................................................27

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LIST OF TABLES

TABLE 1 CIC SPECIFICATIONS...................................................................11

TABLE 2 CFIR AND PFIR STAGE SPECIFICATIONS.......................................11

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INTRODUCTION

The term Radar is the abbreviation for Radio Detection and Ranging, definedas the art of detecting the presence of target, determining their direction andrange, recognizing their character by means of radio waves. The principleinvolved in the atmospheric Radar is to transmit the modulated waveform ofelectromagnetic energy using antenna array into the atmosphere andprocessing the backscattered echoes through suitable means utilizing a chainof signal processors to determine vertical wind components with a high degreeof temporal and spatial resolutions (typically at ~ 30 m and at ~ 150 m,respectively), and other vital parameters required for studying the structuresand dynamics of atmosphere.

Pulsed Doppler-effect radar wind profilers use pulses of electromagneticradiation and exploit the Doppler Effect to measure velocity of the wind atdifferent altitudes from the ground. As the name suggests, these devices areradar systems, albeit somewhat specialized. In particular, they must detectreturned signals at far lower Signal-to-Noise Ratio (SNR) than in conventionalradar systems, since wind speed is very small.

In Radar Applications, the echoes received through the antenna system will bepassed to the RF Receiver system. To bring the signal level within the dynamicrange of Digital Receiver, RF signal will be directly digitized using base bandNyquist criteria. The digitized RF signal will be down converted to base bandlevel using digital down converter (DDC). As the radiated RF signal iscomplimentary coded, base band I and Q data will be passed through theDecoder, then through Coherent integrator, FFT ,Spectral average , Momentsestimation and finally UVW computation, which will give the information aboutwind speed and direction. Over all block diagram is shown below.

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Figure 1 Digital Receiver Overall Block Diagram

This document gives the technical details of the project for the RADAR Signal

Data Acquisition, Conditioning & Processing System. This documentdescribes Functional Features, Functional Description in detail, Scope of Work,Simulation details, Test Environment, Development Schedule andDeliverables.

FUNCTIONAL FEATURES

The proposed system is used for acquisition, conditioning and processing ofWind Profiler RADAR echoes with following functional features:

a) Direct sampling of RF signal by Sub-Nyquist Criteriab) Narrow Band FIR filters in Digital domain (CIC, CFIR and PFIR)c) FPGA based FFT (Max1024 points) for Doppler shift extractiond) FPGA based Standard Decoding algorithm for Coded RF Signale) PC based offline Data processing for U, V and Wf) Displaying the output of all phases on Auxiliary Radar DataDisplay Functional Block Diagram

FUNCTIONAL BLOCK DIAGRAM

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Figure 2: Functional Block Diagram

FUNCTIONAL BLOCK DESCRIPTION

Data Acquisition

To make a receiver more flexible, it is desirable to sample and digitize thereceived signal at the RF frequency. Radio Frequency (RF) front-end is tominimize the complexity and to digitize the signals as close as possible toAntenna. Atmospheric RADAR returns are captured using ICS-1554 DigitalReceiver Card which has a high speed, high resolution ADC. Radio Frequency(RF) front-end digitization is implemented by sampling the 206.5 MHz RFsignal using under-sampling.

Band-pass sampling (or under sampling) is a sampling technique in which thefrequency conversion and the sampling are performed at the same time usinga sampling frequency less than the Nyquist frequency of the sampled

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signal. Using appropriate sampling frequency Fs, the desired signal isfrequency-converted to a frequency range between 0 and Fs/2. Out-bandinterference and noises not rejected by the band pass filter are alsofrequency-converted to the same frequency range. Thus, the signal can befrequency-converted without an oscillator or a mixer by undersampling. The

criterion for band-pass sampling is given as:

Where fH = Higher cut-off frequency, fL = Lower cut-off frequency, and n is aninteger.Acquisition is carried out by taking the features of latest high speed (16-bit)ADC of Linear Technology LTC2209. In the present scheme, the receivedsignals at 206.5 MHz are under-sampled at the speed of 72 MSPS (Fs). Thetranslation is shown in Fig. and the replicated model of LTC2209 illustrating

this translation is shown in Fig. ADC LTC2209 provides an ultra low jitter of 70FS RMS thereby allowing the under sampling of 206.5 MHz RF signal withexcellent noise performance.

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Figure 3 : Band-pass sampling for 206.5 MHzreceived RF signal

Figure 4 : Replication of LTC2209 illustrating band-pass samplingfor 206.5 MHz signal


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The figure below shows the ADC captured Signal of 206.5 MHz RF Signalsampled using LTC2209 ADC with 72 MSPS sample rate on ICS-1554 DigitalReceiver Card.The output spectrum of 9.5 MHz is depicted.

Digital Down Conversion

The DDC performs the critical frequency translation needed to extract thedesired information. It converts a digitized RF signal centered at 206.5 MHzdown to a baseband complex signal centered at zero frequency. In addition todown conversion, DDCs typically decimate to a lower sampling rate, allowingfollow-on signal processing by lower speed processors. The typical blockdiagram of DDC is shown in Fig.

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Figure 6 Framework of DDC section

Figure 5 Real-Time Spectrum of 206.5 MHz RFsignal with Sampling Rate of 72 MSPS.


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DDC block integrates:a) A Direct Digital Synthesizer (DDS)/Numerically Controlled Oscillator (NCO)which generates a complex sinusoid at the intermediate frequency.b) A pair of Mixers that allows translation down to baseband, andc) Chain of multistage multirate filter consisting of Cascaded Integrator Comb(CIC) filter and FIR filters.

DDC can be implemented using either Quad GC4016 ASIC chip on the ICS1554 board or as logic implemented in FPGA fabric. FPGA is a more effectivechoice in the present scheme because of its following merits:

- Better performance like improved Spurious-Free Dynamic Range (SFDR).- Faster processing and inbuilt re-configurability.- Reduction in Power requirement increased precision performance.- Excellent quadrature channel phase balance, increased temperature

stability.- Can easily tailor and optimize the design.

FPGA available in ICS-1554 Digital Rx Card is Virtex-5 SX95T which has 94,208logic cells, 8,784 block RAM bits and 640 DSP Multipliers. In the presentscheme, the digital outputs from the ADC are delivered into the Xilinx Virtex-5

FPGA for routing, formatting and DDC signal processing application. A First-In First- Out (FIFO) interface (128 K x 74 bits) can be provided in between togather data from ADC.NCO is the critical component in DDC. It can beimplemented in a various ways. Here the NCO design based on Look-Up Table(LUT).

The NCO output spectrum is shown in Fig. and the mixer output is shown inFigure.

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Figure 8 NCO Output spectrum Figure 7 Mixer Output spectrum


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The Transfer Function for a CIC filter at Fs is:

For the present scheme, the design specification chosen for CIC filter are givenin Table.

Table 1 CIC Specifications

When there is large number of stages in CIC filter, the frequency responsedoes not possess a flat band character. The magnitude droop occurs in thepass band resulting in undesired response. To overcome the magnitude droop,a FIR filter that has an inverse magnitude response of CIC filter is applied toachieve the frequency response correction. These filters are thereby called asCFIR and PFIR filters. For the present scheme, the design specification chosenfor CFIR and PFIR filters are given in Table.

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Table 2 CFIR and PFIR stage specifications


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The CFIR stage characteristics are shown in Figure and the PFIR stagecharacteristics are shown in Figure.

Resampler can be used as an additional optional filter to optimize the pass

band or stop band response of the channel. The overall gain of DDC is theproduct of the CIC gain, coarse gain, CFIR gain, PFIR gain, final shift gain. Eachof these gain are set by Gain Setting block so as to maximize the signalamplitude without clipping.

Decoding

Received RADAR data could be complementary encoded using 8,16,32,64 bitBinary phase encoding methods such as Barker coding. Decoding algorithmshould be implemented using FPGA. Complementary phase codes are binary intheir simplest form and they usually come in pairs. They are coded exactly as

Barker codes. The range side lobes of the resulting ACF output for each pulsewill generally be larger for a barker code of comparable length,but the two pulses are complementary pair have the property that their sidelobes are equal in magnitude but opposite in sign, so that when outputs areadded the side lobes exactly cancel, leaving only the central peak. Decodingcan be implemented using Modified Transversal Architecture. TransversalFilters can be designed in FPGA using Finite Impulse Response (FIR) Filters.

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Figure 9 CIC stage characteristics

Figure 9: Phase CodedWaveforms and Autocorrelation

Function

Figure 10: AutocorrelationFunction of EDE2 and ED1D

Figure 11: Architecture of TransversalFilter


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Coherent Integration

The detected quadrature signals are coherently integrated for many pulsereturns which lead to an appreciable reduction in the volume of the data to be

processed and an improvement in the SNR. It is integrating a signal for a timeperiod equal to coherence time. Coherence time is the time for whichfrequency of the signal is same. The objective is to measure a signal x (n) ofduration of N samples, n = 0, 1, N-1. The measurement can be performedrepeatedly. A total of M such measurements are performed and the results areaveraged by the signal averaging. Let the results of the mth measurements, form = 1, 2, M, are the samples.

Where, x (n) and n (n) corresponds to signal and noise respectively.

Normalization

The input data is to be normalized by applying a scaling factor correspondingto the operation done on it. This will reduce the chance of data overflowingdue to any other succeeding operation. The Normalization has followingcomponents.

a. sampling resolution of ADCb. scaling due to pulse compression in decoderc. scaling due to coherent integrationd. Scaling due to number of FFT points.

If, v is ADC bit resolution (10/16384),W is Pulse width in microsecond,M is Number of IPP integrated = Integrated time /inter pulse period,N is Number of FFT points,

Then, the Normalization factor is given as

Windowing

To prevent leakage and picket fence effects that occur due to FFT performedon finite length data, weighting the data with suitable windows is performed.However the use of the data windows other than the rectangular windowaffects the bias, variance and frequency resolution of the spectral estimates.

Fourier Analysis

Spectral analysis is connected with characterizing the frequency content of asignal. The windowed spectrum is Fourier transformed to obtain the frequency

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110 =+= Nnfornnxnym ,...,,)()()( 1,.......1,0)(1

)(1

== =

NnfornyM

nxM

m

m


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domain signal. FFT will be performed using the available IP cores for FPGA.From the FFT output, power spectrum is calculated.

Spectral Averaging

Incoherent integration is the averaging of the power spectrum number of

times. The advantage of the incoherent integration is that it improves thedetectability of the Doppler spectrum.

Power Spectrum,

Where, m is the number of spectra integrated.

Spectrum Cleaning

Due to various reasons the radar echoes may get corrupted by ground clutter,system bias, interference, image formation etc. The data is to be cleaned fromthese problems before going for analysis.

Clutter/ DC removal:One way to eliminate its biasing effect is to ignore the frequencies aroundzero (dc) frequency. This is possible only when the spectral offset is largerthan its width.The basic operation carried out here is,

Where, N/2 corresponds to DC or Zero Frequency.

Interference:

Constant frequency bands will form in the power spectrum by the interferencegenerated in the system or due to extraneous signal. Due to this reason it isalso possible the formation of multiple bands in spectrum. This is removed bytaking a range bin, which does not have echoes but the interference. Thisrange bin gets subtracted from all other range bins after the removal of meannoise. If the interference is not affecting the original Doppler trace then theanalysis may be carried out in a window confined to the Doppler trace.

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Noise Level Estimation

The method implemented here is based on the variance decided by a

threshold criterion, Hildebrand and Sekhon (1974). This method makes use ofthe observed Doppler spectrum and of the physical properties of white noise;it does not involve knowledge of the noise level of the radar instrumentsystem. This method is now widely used in atmospheric radar noise thresholdestimation and removal.The noise level threshold shall be estimated to the maximum level L, such thatthe set of Spectral points below the level S, nearly satisfies the criterion,

Step 1:

Reorder the spectrum {Pi, i = 0, N-1} in ascending order to form. Let thissequence be written as {Ai, i = 0, . . . N-1} and Ai < Aj for i < j

Step 2:

Compute,

Where, M is the number of spectra that were averaged for obtaining the data.

Step 3:

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1

.1m in)(

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==RkwherePLlevel N oise n

that su chn

criterioaboveth emeetsnnoifk


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Moments Estimation

An algorithm based on an adaptive method has been used that aims attracking the signal adaptively in a rangeDoppler spectral frame withbackground noise. The algorithm works round a set of parameters that get

updated constantly so as to optimize the tracking performance of the adaptivemethod. The implementation of the method for adaptive tracking of theDoppler signal and estimating moments involves a sequence of steps asdetailed below.

Step 1: a. Noise removal

The raw Doppler power spectra recorded online are subjected to low-passfiltering (that is, smoothing) to reduce the level of noise fluctuations thatappear particularly prominent in the low SNR regions. The low pass filtering isimplemented with a three-point running average of the Doppler spectrum.

Then, the mean noise level is estimated for each range gate using an objectivemethod based on Gaussian statistics (Hildebrand and Sekhon 1974). The meannoise level for each range gate is subtracted from the corresponding powerspectrum.

b. Adaptive signal profiling

The parameters used for adaptive signal tracking in a range-Doppler frame arethe Doppler velocity window, wind shear threshold, and signal-to-noise ratio(SNR).The range-Doppler frame is divided into a number of range windowswith a maximum of 50 range windows per profile and each range windowcontaining two or more range gates. For each range window minimumand maximum velocities and maximum wind shear per range gate areidentified, with allowed margins, and are called the Doppler velocity windowand wind shear threshold, respectively. For the first Doppler frame, as there isno prior information available, the Doppler velocity window and wind shearthreshold parameters are set from range gate to range gate based uponcertain realistic criteria. For subsequent rangeDoppler set the range window,Doppler velocity window, and wind shear threshold parameters. The SNRthreshold is set at 10 dB above the mean noise level estimated for the noiseregion at the upper end of the height range.

Step 2: Setting up of Doppler window and wind shear thresholdparameters

For the first rangeDoppler frame, the Doppler velocity window is setadaptively from range gate to range gate. The window setting is initiated byidentifying the most prominent spectral peak in the first range gate for whichthe SNR is invariably quite high (_7 dB). The Doppler velocity window limits are

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then set at _20% of the coherent integration filter bandwidth on either side ofthe mean Doppler velocity associated with the prominent spectral peak. TheDoppler velocity window set for the first range gate is used to identify themost prominent peak in the second range gate since the signal is notexpected to change by more than 20% of the coherent filter bandwidth from

one range gate to the next. New Doppler velocity window limits are set for thesecond range gate based on the position of the mean Doppler velocity at itsmost prominent spectral peak within the Doppler velocity window. Theprocedure is repeated sequentially for all range gates, thereby fixing a height-varying Doppler velocity windowfor the entire frame. For the first frame, the wind shear threshold is also setfrom range gate to range gate and it is expressed in terms of the deviationpermissible in the equivalent mean Doppler velocity. The wind shear thresholdlimit is set by adding 20% of the full width of the Doppler velocity signal to thelocally computed wind shear using moving pairs of range gates.From the second frame onward the total range is divided into a specified

number of range windows, up to a maximum set of 50 per profile with eachrange window having two or more range gates. Doppler velocity window andwind shear threshold are set from range window to range window. Using theinformation from the previous Doppler frame, for each range windowthe minimum and maximum mean Doppler velocities are noted and theDoppler velocity window is set to the minimum and maximum mean Dopplervelocity with the velocity full width as margin on either side.

Step 3: Selecting five candidate signals and estimating theirmoments

The five most prominent spectral peaks are selected as candidate signalswithin the specified Doppler velocity window for each range gate. For the fivecandidate signals in each range gate, the three low-order spectral momentsare computed following Woodman (1985). The zeroth, first, and secondmoments, representing total signal power, weighted mean Doppler velocityand velocity width, respectively, are denoted by Msm(n), where s varies from 1to 5 and represents the spectral peak number, m varies from 0 to 2 in theorder of the spectral moments, and n is the number of the range gate. Themoments for each range gate are stored in descending order of power level forthe five selected candidate signals.

Step 4: Selecting the most probable candidate

The task performed in this step of the algorithm involves adaptive profiling ofthe Doppler signal through an iterative process. In the first iteration, momentsvalues of the five most prominent candidate signals are selected [i.e., M1m(n)] and stored in the select list if theirSNRs are more than the specified SNR threshold value. In this way the first-cutsignal trace is obtained for the entire height range. The range gates that

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remain unattended in the first iteration based on the SNR criterion aresubsequently dealt with in the following three iterations that make use of thewind shear criterion.

UVW Computation

The prime objective of atmospheric radar is to obtain the vector wind velocity.Velocity measured by radar with the Doppler technique is a line of sightvelocity, which is the projection of velocity vector in the radial direction. There

are two different techniques of determining the three components of thevelocity vector: the Doppler Beam Swinging (DBS) method and SpacedAntenna (SA) method. The DBS method uses a minimum of three radar beamorientations (Vertical, East-West, and North-South) to derive the threecomponents of the wind vector (Vertical, Zonal and Meriodonal). Thehorizontal velocity and the characteristics of the ground diffraction pattern andthereby that of the scattering irregularities can be obtained through the fullcorrelation analysis of Briggs (1984).

Calculation of radial velocity and height:For representing the observation results in physical parameters, the Dopplerfrequency and range bin have to be expressed in terms of correspondingradial velocity and vertical height.

Radial Velocity V = and Vertical Height

where , c - velocity of light in free space, fD- Doppler frequency, fC- Carrierfrequency, l - Carrier wavelength, q - Beam tilt angle, tR - Range time delay.

Computation of absolute Wind velocity vectors (UVW):After computing the radial velocity for different beam positions, the absolutevelocity (UVW) can be calculated. To compute the UVW, at least three non-coplanar beam radial velocity data is required. If higher number of differentbeam data is available, then the computation will give an optimum result inthe least square method.

LOS Velocity Vector V (VX,VY,VZ) for coplanar beam velocity.

VD = V.i = Vx cosx + Vy cosy + Vz cosz

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where X, Y, and Z directions are aligned to East-West, North-South and Zenithrespectively.

Thus, on solving equation we can derive VX,VY,VZ which corresponds to U(Zonal), V (Meridonal) and W (Vertical) components of velocity.

SCOPE OF WORK

The scope of work for the development is divided into three phases.

Phase 1During this phase, Project definition design documents will be submitted. Also,the simulation of spectrum estimation i.e. online signal processing for Dopplerextraction using ICS 1554 card compatible with PCI slot in Windowsenvironment.

Phase 2This phase covers the On-line signal processing of radar return signals onFPGA based PMC card. As part of this phase, the development environment isaround ICS-1554 PMC module with Virtex-5 SX95T FPGA, which is sourced by

ECIL. The input is radar data received as RF signal. Outputs will be the plots ofI and Q channel data, Coherent integrated time domain signal and spectralaveraged power spectrum. GUI will be designed to show the outputs. Thebroad segments of work are

Integration of digital Rx ICS 1554 in a 64 bit PCI-X slot system Digitize the analog RF signal into 16 bit format using LTC 2209 Development of DDC and decoding features on the FPGA in time domain Implementation of coherent signal integration on the FPGA in time

domain Porting of spectral estimation using FFT on FPGA in frequency domain

Phase 3In this phase, mainly off-line processing of Doppler profiles on workstationbased on Linux/RT Linux OS is to be taken up. The input is spectral averagedfrequency domain signal. The broad segments of work involved area) Moments Estimationb) Wind Component Calculationc) Speed and direction calculationd) Real and near time product displays

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e) Data archivingOutputs to be expected of Phase 3 are the plots of1. I, II and III moments estimated (separately for each moment)2. SNR based on moments

3. Doppler4. Doppler width (D-width)5. Noise6. UVW component plots7. Speed8. Direction9. Vertical velocity

SIMULATION DETAILS

I. RF test signal was captured using ICS-1554 Digital Receiver card .The signal

was under-sampled, down converted and transformed to frequency domain by

performing FFT, using Labview.

Sampling frequency of ADC LTC 2209 = 72 MHzRF Signal frequency of radar transmitter = 206.5 MHz

NCO frequency for down conversion = 9.5 MHzWindowing technique for FFT of data = BlackmannNumber of FFT points = 4096

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Figure 11: Raw RF data with frequency206.5MHz

Figure 12: Spectrum of Undersampled signal

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Figure 13: Spectrum of Downconverted signal

II. Simulation model for the On-line processing of Wind profiler Radarsystem is done on MATLAB, for the following design specifications:

Sampling frequency = 72 MHzPulse repetition frequency, PRF=8 kHzWidth of each pulse, w =1e-6 secNumber of pulses generated =250Carrier frequency of radar transmitter=206.5 MHzNCO frequency for down conversion=9.5 MHzMaximum Doppler shift=50 HzNumber of coherent integrated pulse=10Decimation factor=72

Windowing technique=Rectangular

Transmitted pulse:

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Decimation and Filtering:

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In-phase and Quadrature signal:

Coherent Integration:

Normalization:

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Windowing:

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DELIVERABLES

Hardware1. 64 bits PCI-X (PCI-Extended) slots based Work station compatible forICS1554

2. High End processing System for Data Display3. Large screen Auxiliary Display Monitor for presentation Radar Data

SoftwareDigital Signal Processing Source Code for DBS mode implementation.

DEVELOPMENT SCHEDULE

All the activities of phase 1 to be completed within 4 weeks after receiptof order.

Phase 2 to be completed, demonstrated and tested within 10 weeksafter successful completion of phase 1.

Phase 3 to be completed, demonstrated and tested within 15 weeks oncompletion of Phase 1.

TESTING & PROVINGThe system will be proven on bench as well as in the site. The sitefunctionality test will be conducted and certified in the presence ECIL rep. Acomprehensive ATP document for phase 2 & phase 3 will be submitted and thesame to be vetted at ECIL. ECIL is committed to support in the followingareas.

ECIL will provide necessary Input /Output data files required forSimulation and demonstration during Phase-I

ECIL will provide Coherent Signal Generator for testing of ICS-1554Card.CSG will generate RF Signal with desired carrier frequency, ADC clock,and Reference clock.

TRAINING AND DOCUMENTATION

Adequate training to ECIL personnel along with necessary documents will beprovided. ECIL personnel will be involved for each and every stage duringdesign, development of the system. The following documents conforming toIEEE standards will be supplied in Hard Copy & Soft copy in CD.

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1) ATP 1 Nos.2) User Manual 1 Nos.3) Technical Manual 1 Nos.

OTHER REQUIREMENTS:The design and development will conform to General Radar Signal Processingstandards.The integration of the system and demonstration of all the essential featureswill be carried out. In addition, System support will be available for at leastthree years after installation and commissioning. Training for 4 persons : 7 working Days Warranty and Support : 1 years

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RADAR Signal Data Acquisition, Conditioning & Processing System

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