FPGA DESIGN USING MATLAB

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1 FPGA DESIGN USING MATLAB Our online Tutors are available 24*7 to provide Help with FPGA Design Homework/Assignment or a long term Graduate/Undergraduate FPGA Design Project. Our Tutors being experienced and proficient in FPGA Design ensure to provide high quality FPGA Design Homework Help. Upload your FPGA Design Assignment at Submit Your Assignment button or it to info@assignmentpedia.com. You can use our Live Chat option to schedule an Online Tutoring session with our FPGA Design Tutors. SCAN RADAR USING A UNIFORM RECTANGULAR ARRAY This example simulates a phased array radar that periodically scans a predefined surveillance region. A 900-element rectangular array is used in this monostatic radar. Steps are introduced to derive the radar parameters according to specifications. After synthesizing the received pulses, detection and range estimation are performed. Finally, Doppler estimation is used to obtain the speed of each target. pd = 0.9; % Probability of detection pfa = 1e-6; % Probability of false alarm max_range = 5000; % Maximum unambiguous range tgt_rcs = 1; % Required target radar cross section int_pulsenum = 10; % Number of pulses to integrate Load the radar system and retrieve system parameters. load basicmonostaticradardemodata; fc = hradiator.operatingfrequency; % Operating frequency (Hz) v = hradiator.propagationspeed; % Wave propagation speed (m/s) lambda = v/fc; % Wavelength (m) fs = hwav.samplerate; % Sampling frequency (Hz) prf = hwav.prf; % Pulse repetition frequency (Hz) Next, we define a 30-by-30 uniform rectangular array. wsura = warning('off', 'phased:system:array:sizeconventionwarning'); harray = phased.ura('element',hant,... 'Size',[30 30],'ElementSpacing',[lambda/2, lambda/2]); % Configure the antenna elements such that they only transmit forward harray.element.backbaffled = true; % Visualize the response pattern. plotresponse(harray,fc,physconst('lightspeed'),... 'RespCut','3D','Format','Polar');

2 Associate the array with the radiator and collector. hradiator.sensor = harray; hcollector.sensor = harray; % We need to set the WeightsInputPort property to true to enable it to % accept transmit beamforming weights hradiator.weightsinputport = true; Now we need to recalculate the transmit power. The original transmit power was calculated based on a single antenna. For a 900-element array, the power required for each element is much less. % Calculate the array gain hag = phased.arraygain('sensorarray',harray,'propagationspeed',v); ag = step(hag,fc,[0;0]); % Use the radar equation to calculate the peak power snr_min = albersheim(pd, pfa, int_pulsenum); peak_power = radareqpow(lambda,max_range,snr_min,hwav.pulsewidth,... 'RCS',tgt_rcs,'Gain',htx.Gain + ag) peak_power =

3 The new peak power is Watts. % Set the peak power of the transmitter htx.peakpower = peak_power; We also need to design the scanning schedule of the phased array. To simplify the example, we only search in the azimuth dimension. We require the radar to search from 45 degrees to -45 degrees in azimuth. The revisit time should be less than 1 second, meaning that the radar should revisit the same azimuth angle within 1 second. initialaz = 45; endaz = -45; volumnaz = initialaz - endaz; To determine the required number of scans, we need to know the beamwidth of the array response. We use an empirical formula to estimate the 3-dB beamwidth. where is the array gain and is the 3-dB beamwidth. % Calculate 3-dB beamwidth theta = radtodeg(sqrt(4*pi/db2pow(ag))) theta = The 3-dB beamwidth is 6.77 degrees. To allow for some beam overlap in space, we choose the scan step to be 6 degrees. scanstep = -6; scangrid = initialaz+scanstep/2:scanstep:endaz; numscans = length(scangrid); pulsenum = int_pulsenum*numscans; % Calculate revisit time revisittime = pulsenum/prf

4 revisittime = The resulting revisit time is second, well below the prescribed upper limit of 1 second. Target Definition We want to simulate the pulse returns from two non-fluctuating targets, both at 0 degrees elevation. The first target is approaching to the radar, while the second target is moving away from the radar. htarget{1} = phased.radartarget(... 'MeanRCS',1.6,... 'OperatingFrequency',fc); htargetplatform{1} = phased.platform(... 'InitialPosition',[ ; 800; 0],... 'Velocity',[-100; 50; 0]); % Calculate the range, angle, and speed of the first target [tgt1_rng,tgt1_ang] = rangeangle(htargetplatform{1}.initialposition,... hantplatform.initialposition); tgt1_speed = radialspeed(htargetplatform{1}.initialposition,... htargetplatform{1}.velocity,hantplatform.initialposition); htarget{2} = phased.radartarget(... 'MeanRCS',1.2,... 'OperatingFrequency',fc); htargetplatform{2} = phased.platform(... 'InitialPosition',[ ; 0; 0],... 'Velocity',[60; 80; 0]); % Calculate the range, angle, and speed of the second target [tgt2_rng,tgt2_ang] = rangeangle(htargetplatform{2}.initialposition,... hantplatform.initialposition); tgt2_speed = radialspeed(htargetplatform{2}.initialposition,... htargetplatform{2}.velocity,hantplatform.initialposition); numtargets = length(htarget); Pulse Synthesis Now that all subsystems are defined, we can proceed to simulate the received signals. The total simulation time corresponds to one pass through the surveillance region. Because the reflected signals are received by an array, we use a beamformer pointing to the steering direction to obtain the combined signal. % Create the steering vector for transmit beamforming

5 hsv = phased.steeringvector('sensorarray',harray,'propagationspeed',v); % Create the receiving beamformer hbf = phased.phaseshiftbeamformer('sensorarray',harray,... 'OperatingFrequency',fc,'PropagationSpeed',v,... 'DirectionSource','Input port'); % Define propagation channel for each target for n = numtargets:-1:1 htargetchannel{n} = phased.freespace(... 'SampleRate',fs,... 'TwoWayPropagation',true,... 'OperatingFrequency',fc); end fast_time_grid = unigrid(0, 1/fs, 1/prf, '[)'); rx_pulses = zeros(numel(fast_time_grid),pulsenum); % Pre-allocate tgt_ang = zeros(2,numtargets); % Target angle for m = 1:pulsenum x = step(hwav); % Generate pulse [s, tx_status] = step(htx,x); % Transmit pulse [ant_pos,ant_vel] = step(hantplatform,1/prf);% Update antenna position % Calculate the steering vector scanid = floor((m-1)/int_pulsenum) + 1; sv = step(hsv,fc,scangrid(scanid)); w = conj(sv); rsig = zeros(length(s),numtargets); for n = numtargets:-1:1 % For each target [tgt_pos,tgt_vel] = step(... htargetplatform{n},1/prf); % Update target position [~,tgt_ang(:,n)] = rangeangle(tgt_pos,...% Calculate range/angle ant_pos); tsig = step(hradiator,s,tgt_ang(:,n),w); % Radiate toward target tsig = step(htargetchannel{n},... % Propagate pulse tsig,ant_pos,tgt_pos,ant_vel,tgt_vel); rsig(:,n) = step(htarget{n},tsig); % Reflect off target end rsig = step(hcollector,rsig,tgt_ang); % Collect all echoes rsig = step(hrx,rsig,~(tx_status>0)); % Receive signal rsig = step(hbf,rsig,[scangrid(scanid);0]); % Beamforming rx_pulses(:,m) = rsig; % Form data matrix end Matched Filter

6 To process the received signal, we first pass it through a matched filter, then integrate all pulses for each scan angle. % Matched filtering matchingcoeff = getmatchedfilter(hwav); hmf = phased.matchedfilter(... 'Coefficients',matchingcoeff,... 'GainOutputPort',true); [mf_pulses, mfgain] = step(hmf,rx_pulses); mf_pulses = reshape(mf_pulses,[],int_pulsenum,numscans); matchingdelay = size(matchingcoeff,1)-1; sz_mfpulses = size(mf_pulses); mf_pulses = [mf_pulses(matchingdelay+1:end) zeros(1,matchingdelay)]; mf_pulses = reshape(mf_pulses,sz_mfpulses); % Pulse integration int_pulses = pulsint(mf_pulses,'noncoherent'); int_pulses = squeeze(int_pulses); % Visualize r = v*fast_time_grid/2; X = r'*cosd(scangrid); Y = r'*sind(scangrid); clf; pcolor(x,y,pow2db(abs(int_pulses).^2)); axis equal tight shading interp set(gca,'visible','off'); text(-800,0,'array'); text((max(r)+10)*cosd(initialaz),(max(r)+10)*sind(initialaz),... [num2str(initialaz) '^o']); text((max(r)+10)*cosd(endaz),(max(r)+10)*sind(endaz),... [num2str(endaz) '^o']); text((max(r)+10)*cosd(0),(max(r)+10)*sind(0),[num2str(0) '^o']); colorbar;

7 From the scan map, we can clearly see two peaks. The close one is at around 0 degrees azimuth, the remote one at around 10 degrees in azimuth. Detection and Range Estimation To obtain an accurate estimation of the target parameters, we apply threshold detection on the scan map. First we need to compensate for signal power loss due to range by applying time varying gains to the received signal. range_gates = v*fast_time_grid/2; htvg = phased.timevaryinggain(... 'RangeLoss',2*fspl(range_gates,lambda),... 'ReferenceLoss',2*fspl(max(range_gates),lambda)); tvg_pulses = step(htvg,mf_pulses); % Pulse integration int_pulses = pulsint(tvg_pulses,'noncoherent'); int_pulses = squeeze(int_pulses); % Calculate the detection threshold npower = noisepow(hrx.noisebandwidth,... hrx.noisefigure,hrx.referencetemperature); threshold = npower * db2pow(npwgnthresh(pfa,int_pulsenum,'noncoherent')); % Increase the threshold by the matched filter processing gain

8 threshold = threshold * db2pow(mfgain); We now visualize the detection process. To better represent the data, we only plot range samples beyond 50. N = 51; clf; surf(x(n:end,:),y(n:end,:),... pow2db(abs(int_pulses(n:end,:)).^2)); hold on; mesh(x(n:end,:),y(n:end,:),... pow2db(threshold*ones(size(x(n:end,:)))),'facealpha',0.8); view(0,56); set(gca,'visible','off'); There are two peaks visible above the detection threshold, corresponding to the two targets we defined earlier. We can find the locations of these peaks and estimate the range and angle of each target. [I,J] = find(abs(int_pulses).^2 > threshold); est_range = range_gates(i); % Estimated range est_angle = scangrid(j); % Estimated direction

9 Doppler Estimation Next, we want to estimate the Doppler speed of each target. For details on Doppler estimation, refer to the example Doppler Estimation. for m = 2:-1:1 [p, f] = periodogram(mf_pulses(i(m),:,j(m)),[],256,prf,... 'power','centered'); speed_vec = dop2speed(f,lambda)/2; spectrum_data = p/max(p); [~,dop_detect1] = findpeaks(pow2db(spectrum_data),'minpeakheight',-5); sp(m) = speed_vec(dop_detect1); end warning(wsura); Finally, we have estimated all the parameters of both detected targets. Below is a comparison of the estimated and true parameter values Estimated (true) target parameters Range (m) Azimuth (deg) Speed (m/s) Target 1: ( ) (12.76) (86.49) Target 2: ( ) 0.00 (0.00) (-60.00) Summary In this example, we showed how to simulate a phased array radar to scan a predefined surveillance region. We illustrated how to design the scanning schedule. A conventional beamformer was used to process the received multi-channel signal. The range, angle, and Doppler information of each target are extracted from the reflected pulses. This information can be used in further tasks such as high resolution direction-of-arrival estimation, or target tracking. visit us at or us at info@assignmentpedia.com or call us at