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.
First we create a phased array radar. We reuse most of the subsystems built in the example Simulating Test Signals for a Radar Receiver. Readers are encouraged to explore the details of radar system design through that example. A major difference is that we use a 30-by-30 uniform rectangular array (URA) in place of the original single antenna.
The existing radar design meets the following specifications.
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 BasicMonostaticRadarExampleData; fc = radiator.OperatingFrequency; % Operating frequency (Hz) v = radiator.PropagationSpeed; % Wave propagation speed (m/s) lambda = v/fc; % Wavelength (m) fs = waveform.SampleRate; % Sampling frequency (Hz) prf = waveform.PRF; % Pulse repetition frequency (Hz)
Next, we define a 30-by-30 uniform rectangular array.
ura = phased.URA('Element',antenna,... 'Size',[30 30],'ElementSpacing',[lambda/2, lambda/2]); % Configure the antenna elements such that they only transmit forward ura.Element.BackBaffled = true; % Visualize the response pattern. pattern(ura,fc,'PropagationSpeed',physconst('LightSpeed'),... 'Type','powerdb');
Associate the array with the radiator and collector.
radiator.Sensor = ura; collector.Sensor = ura; % We need to set the WeightsInputPort property to true to enable it to % accept transmit beamforming weights radiator.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 arraygain = phased.ArrayGain('SensorArray',ura,'PropagationSpeed',v); ag = arraygain(fc,[0;0]); % Calculate the peak power snr_min = albersheim(pd, pfa, int_pulsenum); peak_power = ((4*pi)^3*noisepow(1/waveform.PulseWidth)*max_range^4*... db2pow(snr_min))/(db2pow(2*(transmitter.Gain+ag))*tgt_rcs*lambda^2)
peak_power = 0.0065
The new peak power is 0.0065 Watts.
% Set the peak power of the transmitter transmitter.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 = 6.7703
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
revisitTime = 0.0050
The resulting revisit time is 0.005 second, well below the prescribed upper limit of 1 second.
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.
tgtpos = [[3532.63; 800; 0],[2020.66; 0; 0]]; tgtvel = [[-100; 50; 0],[60; 80; 0]]; tgtmotion = phased.Platform('InitialPosition',tgtpos,'Velocity',tgtvel); tgtrcs = [1.6 2.2]; target = phased.RadarTarget('MeanRCS',tgtrcs,'OperatingFrequency',fc); % Calculate the range, angle, and speed of the targets [tgtrng,tgtang] = rangeangle(tgtmotion.InitialPosition,... sensormotion.InitialPosition); numtargets = length(target.MeanRCS);
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 steeringvec = phased.SteeringVector('SensorArray',ura,... 'PropagationSpeed',v); % Create the receiving beamformer beamformer = phased.PhaseShiftBeamformer('SensorArray',ura,... 'OperatingFrequency',fc,'PropagationSpeed',v,... 'DirectionSource','Input port'); % Define propagation channel for each target channel = phased.FreeSpace(... 'SampleRate',fs,... 'TwoWayPropagation',true,... 'OperatingFrequency',fc); fast_time_grid = unigrid(0, 1/fs, 1/prf, '[)'); % Pre-allocate array for improved processing speed rxpulses = zeros(numel(fast_time_grid),pulsenum); for m = 1:pulsenum % Update sensor and target positions [sensorpos,sensorvel] = sensormotion(1/prf); [tgtpos,tgtvel] = tgtmotion(1/prf); % Calculate the target angles as seen by the sensor [tgtrng,tgtang] = rangeangle(tgtpos,sensorpos); % Calculate steering vector for current scan angle scanid = floor((m-1)/int_pulsenum) + 1; sv = steeringvec(fc,scangrid(scanid)); w = conj(sv); % Form transmit beam for this scan angle and simulate propagation pulse = waveform(); [txsig,txstatus] = transmitter(pulse); txsig = radiator(txsig,tgtang,w); txsig = channel(txsig,sensorpos,tgtpos,sensorvel,tgtvel); % Reflect pulse off of targets tgtsig = target(txsig); % Beamform the target returns received at the sensor rxsig = collector(tgtsig,tgtang); rxsig = receiver(rxsig,~(txstatus>0)); rxpulses(:,m) = beamformer(rxsig,[scangrid(scanid);0]); end
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(waveform); matchedfilter = phased.MatchedFilter(... 'Coefficients',matchingcoeff,... 'GainOutputPort',true); [mf_pulses, mfgain] = matchedfilter(rxpulses); 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 axis 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;
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.
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; tvg = phased.TimeVaryingGain(... 'RangeLoss',2*fspl(range_gates,lambda),... 'ReferenceLoss',2*fspl(max(range_gates),lambda)); tvg_pulses = tvg(mf_pulses); % Pulse integration int_pulses = pulsint(tvg_pulses,'noncoherent'); int_pulses = squeeze(int_pulses); % Calculate the detection threshold % sample rate is twice the noise bandwidth in the design system noise_bw = receiver.SampleRate/2; npower = noisepow(noise_bw,... receiver.NoiseFigure,receiver.ReferenceTemperature); threshold = npower * db2pow(npwgnthresh(pfa,int_pulsenum,'noncoherent')); % Increase the threshold by the matched filter processing gain 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); axis 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.
[~,peakInd] = findpeaks(int_pulses(:),'MinPeakHeight',sqrt(threshold)); [rngInd,angInd] = ind2sub(size(int_pulses),peakInd); est_range = range_gates(rngInd); % Estimated range est_angle = scangrid(angInd); % Estimated direction
Next, we want to estimate the Doppler speed of each target. For details on Doppler estimation, refer to the example Doppler Estimation.
for m = numtargets:-1:1 [p, f] = periodogram(mf_pulses(rngInd(m),:,angInd(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); % Estimated Doppler speed end
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: 3625.00 (3622.08) 12.00 (12.76) 86.01 (86.49) Target 2: 2025.00 (2020.66) 0.00 (0.00) -59.68 (-60.00)
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.