Visualizing Radar and Target Trajectories in System Simulation
This example shows how to use scenario viewer to visualize the radar system theater.
A radar system simulation often includes many moving objects. For example, both the radar and the targets can be in motion. In addition, each moving object may have its own orientations, so the bookkeeping becomes more and more challenging when more players present in the simulation.
Phased Array System Toolbox™ provides a scenario viewer to help visualize how radars and targets are moving in space. Through the scenario viewer, one can follow the trajectory of each moving platform and examine the relative motion between the radar and the target.
In the first example, the scenario viewer is used to visualize the trajectories of a radar and a target. Assume that the radar is circling around the origin at 3 km away. The plane with radar flies at 250 m/s (about 560 mph), and makes a circle approximately every 60 seconds.
v = 250; deltaPhi = 360/60; sensormotion = phased.Platform(... 'InitialPosition',[0;-3000;500],... 'VelocitySource','Input port',... 'InitialVelocity',[0;v;0]);
The target is traveling along a straight road with a velocity of 30 m/s along the x axis. which is approximately 67 mph.
tgtmotion = phased.Platform('InitialPosition',[0;0;0],... 'Velocity',[30;0;0]);
The viewer is set to update at every 0.1 second. For the simplest case, the beam is not shown in the viewer.
tau = 0.5; sceneview = phased.ScenarioViewer('ShowBeam','None');
The following code simulates and displays radar and target's trajectories.
for m = 1:tau:60 [sensorpos,sensorvel] = sensormotion(tau,... v*[cosd(m*deltaPhi);sind(m*deltaPhi);0]); [tgtpos,tgtvel] = tgtmotion(tau); sceneview(sensorpos,sensorvel,tgtpos,tgtvel); drawnow; end
Visualize Trajectories and Radar Beams
Next natural step is to visualize the radar beams together with the trajectories in the viewer. The following example shows how to visualize two radars and three targets moving in space. In particular. the first radar has a beam tracking the first target.
First, setup radars and targets. Note the first radar and the first target match what used in the previous section.
sensormotion = phased.Platform(... 'InitialPosition',[0 0;-3000 500;500 1], ... 'VelocitySource','Input port', ... 'InitialVelocity',[0 100;v 0;0 0], ... 'OrientationAxesOutputPort', true); tgtmotion = phased.Platform(... 'InitialPosition',[0 2000.66 3532.63;0 0 500;0 500 500],... 'Velocity',[30 120 -120; 0 0 -20; 0 0 60],... 'OrientationAxesOutputPort', true);
To properly point the beam, the scenario viewer needs to know the orientation information of radars and targets. Such information can be obtained from these moving platforms by setting the OrientationAxesOutputPort property to true at each simulation step, as shown in the code above. To pass this information to the viewer, set the scenario viewer's OrientationInputPort property to true.
sceneview = phased.ScenarioViewer('BeamRange',[3e3 3e3],... 'BeamWidth',[5 5], ... 'ShowBeam', 'All', ... 'CameraPerspective', 'Custom', ... 'CameraPosition', [-15453.85 -19716.96 13539], ... 'CameraOrientation', [-47 -27 0]', ... 'CameraViewAngle', 11.28, ... 'OrientationInputPort', true, ... 'UpdateRate',1/tau);
Note the displayed beam has a beamwidth of 5 degrees and a length of 3 km. The camera perspective is also adjusted to visualize all trajectories more clearly.
for m = 1:60 [sensorpos,sensorvel,sensoraxis] = sensormotion(tau,... [v*[cosd(m*deltaPhi);sind(m*deltaPhi);0] [100; 0; 0]]); [tgtpos,tgtvel,tgtaxis] = tgtmotion(tau); % Radar 1 tracks Target 1 [lclrng, lclang] = rangeangle(tgtpos(:,1),sensorpos(:,1),... sensoraxis(:,:,1)); % Update beam direction sceneview.BeamSteering = [lclang [0;0]]; sceneview(sensorpos,sensorvel,sensoraxis,tgtpos,tgtvel,tgtaxis); drawnow; end
System Simulation Visualization
The scenario viewer can also be combined together with other visualizations to provide more information of the system under simulation. Next example uses a scenario viewer together with a range time intensity (RTI) scope and a Doppler time intensity (DTI) scope so an engineer can examine whether the estimated ranges and range rates of targets match the ground truth.
The example uses the radar system created in the docid:phased_examples.example-ex97528254 example.
Consider the scene where there are three targets.
fc = radiator.OperatingFrequency; fs = waveform.SampleRate; c = radiator.PropagationSpeed; sensormotion = phased.Platform(... 'InitialPosition',[0; 0; 10],... 'Velocity',[0; 0; 0]); target = phased.RadarTarget(... 'MeanRCS',[1.6 2.2 1.05],... 'OperatingFrequency',fc); tgtmotion = phased.Platform(... 'InitialPosition',[2000.66 3532.63 3845.04; 0 0 0;10 10 10], ... 'Velocity',[120 -120 0; 0 0 0; 0 0 0]); channel = phased.FreeSpace(... 'SampleRate',fs,... 'TwoWayPropagation',true,... 'OperatingFrequency',fc);
Once the echo arrives at the receiver, a matched filter and a pulse integrator is used to perform the range estimation.
matchingcoeff = getMatchedFilter(waveform); matchingdelay = size(matchingcoeff,1)-1; matchedfilter = phased.MatchedFilter(... 'Coefficients',matchingcoeff,... 'GainOutputPort',true); prf = waveform.PRF; fast_time_grid = unigrid(0,1/fs,1/prf,'[)'); rangeGates = c*fast_time_grid/2; lambda = c/fc; max_range = 5000; tvg = phased.TimeVaryingGain(... 'RangeLoss',2*fspl(rangeGates,lambda),... 'ReferenceLoss',2*fspl(max_range,lambda)); num_pulse_int = 10;
Because it is unnecessary to monitor the trajectory at the pulse repetition rate, this example assumes that the system reads the radar measurement at a rate of 20 Hz. The example uses a scenario viewer to monitor the scene and a range time intensity (RTI) plot as well as a Doppler time intensity (DTI) plot to examine the estimated range and range rate value.
r_update = 20; sceneview = phased.ScenarioViewer('UpdateRate',r_update,... 'Title','Monostatic Radar with Three Targets'); rtiscope = phased.IntensityScope('Name','Range-Time Intensity Scope',... 'XLabel','Range (m)', ... 'XResolution',c/(2*fs), ... 'XOffset',-(matchingdelay-1)*c/(2*fs), ... 'TimeResolution',1/r_update,'TimeSpan',5,'IntensityUnits','dB'); nfft = 128; df = prf/nfft; dtiscope = phased.IntensityScope(... 'Name','Doppler-Time Intensity Scope',... 'XLabel','Velocity (m/sec)', ... 'XResolution',dop2speed(df,lambda)/2, ... 'XOffset', dop2speed(-prf/2,lambda)/2, ... 'TimeResolution',1/r_update,'TimeSpan',5,'IntensityUnits','dB');
Next section performs the system simulation and produces the visualizations.
% Pre-allocate array for improved processing speed rxpulses = zeros(numel(rangeGates),num_pulse_int); for k = 1:100 for m = 1:num_pulse_int % Update sensor and target positions [sensorpos,sensorvel] = sensormotion(1/prf); [tgtpos,tgtvel] = tgtmotion(1/prf); % Calculate the target angles as seen by the sensor [~,tgtang] = rangeangle(tgtpos,sensorpos); % Simulate propagation of pulse in direction of targets pulse = waveform(); [txsig,txstatus] = transmitter(pulse); txsig = radiator(txsig,tgtang); txsig = channel(txsig,sensorpos,tgtpos,sensorvel,tgtvel); % Reflect pulse off of targets tgtsig = target(txsig); % Receive target returns at sensor rxsig = collector(tgtsig,tgtang); rxpulses(:,m) = receiver(rxsig,~(txstatus>0)); end rxpulses = matchedfilter(rxpulses); % Correct for matched filter delay rxpulses = buffer(... rxpulses(matchingdelay+1:end),... size(rxpulses,1)); rxpulses = tvg(rxpulses); rx_int = pulsint(rxpulses,'noncoherent'); % display RTI rtiscope(rx_int); % display DTI rx_dop = mean(fftshift(... abs(fft(rxpulses,nfft,2)),2)); dtiscope(rx_dop.'); % display scene sceneview(sensorpos,sensorvel,tgtpos,tgtvel); % perform next detection when next update is needed sensormotion(1/r_update); tgtmotion(1/r_update); end hide(dtiscope); hide(rtiscope);
Both the scenario viewer and the RTI are updated during the simulation so one can easily verify whether the simulation is running as expected and whether the range estimation matches the ground truth while the simulation is running.
Similarly, the DTI provides the range rate estimates of each target.
This example describes different ways to visualize the radar and target's trajectories. Such visualizations help provide an overall picture of the system dynamics.