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Simulate FMCW Interference Between Automotive Radars

This example shows how to simulate interference between two FMCW automotive radars in a highway scenario. Missed and ghost detections caused by interferences can be simulated and visualized. Multipath propagation effects between vehicles are taken into account.


With the increasing number of vehicles being equipped with multiple radars in automotive scenarios, interference detection and mitigation have become essential for automotive radars. Interference can affect the functionality of radar systems in many different ways. Interference can increase the noise level of the CFAR detection algorithm and decrease the probability of detection. The signal to interference and noise ratio (SINR) depends on the distance and angles separating two radars, as well as radar parameters such as antenna gain, antenna transmit power, bandwidth, center frequency, and chirp duration. Low SINR results in missed detections for low RCS targets, creating a blind spot in some regions.

Another challenge with radar interference is ghost targets, in which the radar detects an object that doesn't exist. It may happen when the victim radar and interfering radar have similar waveforms. The victim radar can mitigate the probability of false alarms caused by ghost targets by changing the waveform timing parameters on a frame-to-frame basis. The false-alarm probability can also be mitigated by a tracking algorithm on the victim's radar.

Define Victim FMCW Radar Waveform

The victim radar is designed to use an FMCW waveform which is a common waveform in automotive applications because it enables range and Doppler estimation through computationally efficient FFT operations. For illustration purposes, in this example, configure the radar to a maximum range of 150 m. The victim radar operates at a 77 GHz frequency. The radar is required to resolve objects that are at least 1 meter apart. Because this is a forward-facing radar application, the radar also needs to handle targets with large closing speeds as high as 230 km/h.

rng(2017);             % Set random number generator for repeatable results
fc = 77e9;             % Center frequency (Hz)
c = physconst('LightSpeed');             % Speed of light (m/s)
lambda = freq2wavelen(fc,c);             % Wavelength (m)
rangeMax = 150;        % Maximum range (m)
rangeRes = 1;          % Desired range resolution (m)
vMax = 230*1000/3600;  % Maximum velocity of cars (m/s)
fmcwwav1 = generateFMCWWaveform(fc,rangeMax,rangeRes,vMax);
sig = fmcwwav1();
Ns = numel(sig);

Define Interfering FMCW Radar Waveform

Consider an interfering radar using FMCW modulation. The frequency spectrum characteristics of the interference in the victim radar depend on the chirp's relative timing and slopes.

fcRdr2 = 77e9;         % Center frequency (Hz)
lambdaRdr2 = freq2wavelen(fcRdr2,c);              % Wavelength (m)
rangeMaxRdr2 = 100;    % Maximum range (m)
rangeResRdr2 = 0.8;     % Desired range resolution (m)
vMaxRdr2 = vMax;       % Maximum Velocity of cars (m/s)
fmcwwav2 = generateFMCWWaveform(fcRdr2,rangeMaxRdr2,rangeResRdr2,vMaxRdr2);
sigRdr2 = fmcwwav2();

Plot the victim radar and interfering radar waveforms:

pspectrum(repmat(sig,3,1),fmcwwav1.SampleRate,'spectrogram', ...
axis([0 15 -80 80]); title('Victim Radar'); colorbar off

Figure contains an axes object. The axes object with title Victim Radar contains an object of type image.

axis([0 15 -80 80]); title('Interfering Radar'); colorbar off;

Figure contains an axes object. The axes object with title Interfering Radar contains an object of type image.

Model Victim and Interfering Radar Transceivers

Consider both radars use an isotropic element to transmit and a uniform linear array (ULA) to receive the radar waveforms. Using a linear array enables the radar to estimate the azimuthal direction of the reflected energy received from the target vehicles. The long-range radar needs to detect targets across a coverage area of 45 degrees in front of the ego vehicle.

% Construct the array for Victim Radar
Nve = 2*3;                                      % Number of virtual antenna with 2Tx, 3Rx
antElmnt = phased.IsotropicAntennaElement('BackBaffled',false);
rxVArray = phased.ULA('Element',antElmnt,'NumElements',Nve,'ElementSpacing',freq2wavelen(fc)/2,'Taper',taylorwin(Nve));

antAperture = 6.06e-4;                          % Antenna aperture (m^2)
antGain = aperture2gain(antAperture,lambda);    % Antenna gain (dB)
txPkPower = db2pow(13)*1e-3;                     % Tx peak power (W)
rxNF = 4.5;                                     % Receiver noise figure (dB)
% Model Victim Radar Transceiver
vradar = radarTransceiver("Waveform",fmcwwav1);
vradar.Transmitter = phased.Transmitter('PeakPower',txPkPower,'Gain',antGain);
vradar.TransmitAntenna = phased.Radiator('Sensor',antElmnt,'OperatingFrequency',fc);
vradar.ReceiveAntenna = phased.Collector('Sensor',rxVArray,'OperatingFrequency',fc);
vradar.Receiver = phased.ReceiverPreamp('Gain',antGain,'NoiseFigure',rxNF,'SampleRate',fmcwwav1.SampleRate);

% Define Victim Radar
VictimRadar = radarTransceiver('Waveform',fmcwwav1,'Transmitter',vradar.Transmitter, ...
    'TransmitAntenna',vradar.TransmitAntenna,'ReceiveAntenna',vradar.ReceiveAntenna ,'Receiver',vradar.Receiver);

Model the transceiver of the interfering radar as a clone of the victim radar using different FMCW parameters.

% Model Interfering Radar Transceiver
iradar = clone(vradar);
iradar.Waveform = fmcwwav2;
vradar.Receiver = phased.ReceiverPreamp('Gain',antGain,'NoiseMethod','Noise power','NoisePower',0);

Define Range Doppler Response

The radar collects multiple sweeps of the waveform on each of the linear phased array antenna elements. These collected sweeps form a data cube, which is defined in Radar Data Cube. These sweeps are coherently processed along the fast- and slow-time dimensions of the data cube to estimate the range and Doppler of the vehicles.

Use the phased.RangeDopplerResponse object to perform the range and Doppler processing on the radar data cubes. Use a Hann window to suppress the large sidelobes produced by the vehicles when they are close to the radar.

Nft = fmcwwav1.SweepTime*fmcwwav1.SampleRate; % Number of fast-time samples
Nsweep = 192;                                 % Number of slow-time samples
Nr = 2^nextpow2(Nft);                         % Number of range samples
Nd = 2^nextpow2(Nsweep);                      % Number of Doppler samples
rngdopresp = phased.RangeDopplerResponse('RangeMethod','FFT', ...
    'DopplerOutput','Speed','SweepSlope',fmcwwav1.SweepBandwidth/fmcwwav1.SweepTime, ...
    'RangeFFTLengthSource','Property','RangeFFTLength',Nr, ...
    'RangeWindow','Hann', ...
    'DopplerFFTLengthSource','Property','DopplerFFTLength',Nd, ...
    'DopplerWindow','Hann', ...

Model Free Space Propagation Channel for Interference

Use the free space channel to model the propagation of the transmitted and received signals for the interfering radar. In a free space model, the radar energy propagates along a direct line-of-sight between radars and the target vehicles.

% Create a multipath channel for interfering radar
channelIradar = helperCreateMultiPathChannel('SampleRate',fmcwwav2.SampleRate, ...

Simulate Driving Scenario

Create a highway driving scenario with two vehicles traveling in the vicinity of the ego vehicle. The vehicles are modeled as cuboids and have different velocities and positions defined in the driving scenario. The ego vehicle is moving with a velocity of 25 m/s and the other two vehicles are both moving at 50 m/s in the opposite direction. For details on modeling a driving scenario see the example Create Actor and Vehicle Trajectories Programmatically (Automated Driving Toolbox). The radar sensor is mounted on the front of the ego and interferer vehicles.

To create the driving scenario, use the helperAutoDrivingScenario function. To examine the contents of this function, use the edit('helperAutoDrivingScenario') command.

% Create driving scenario
[scenario, egoCar] = helperAutoDrivingScenario;

The following loop uses the drivingScenario object to advance the vehicles in the scenario. A radar data cube is assembled at every simulation time step by collecting 192 sweeps of the radar waveform. The assembled data cube is then processed in range and Doppler.

% Run the simulation loop
sweepTime = fmcwwav1.SweepTime;

while advance(scenario)

    % Get the current scenario time
    time = scenario.SimulationTime;

    % Get current target poses in ego vehicle's reference frame
    tgtPoses = targetPoses(egoCar);
    actProf = actorProfiles(scenario);

    % Assemble data cube at current scenario time
    Xcube = zeros(Nft,Nve,Nsweep);
    XcubeVradar = zeros(Nft,Nve,Nsweep);

    % Generate waveform signal to transmit for interfering radar
    sIsig = step(iradar.Waveform);

    % Match the length of interfering signal with victim sweep
    sIsig = repmat(sIsig,ceil(Nft/length(sIsig)),1);

    for m = 1:Nsweep
        sIcar = sIsig(1:Nft);
        sIsig = circshift(sIsig,length(sIsig)-Nft);

        % Generate all paths to victim radar
        [vpaths, ipaths] = helperGeneratePaths(tgtPoses,actProf,[lambda lambdaRdr2]);  % Global to local coord system(s) inside

        % Send signal from interfering radar to power amplifier for transmit
        IcarSig = step(iradar.Transmitter,sIcar);

        % Radiate signal from antenna elements of interfering radar
        xtIcar = step(iradar.TransmitAntenna,IcarSig,[ ipaths(1).AngleOfDeparture ipaths(2).AngleOfDeparture]);

        % Propagate signal along paths
        [xrxIradar,angIradar ]= step(channelIradar, xtIcar, ipaths,time);

        % Pass the signals through the receive antenna array elements
        xcIradar = step(vradar.ReceiveAntenna, xrxIradar, angIradar);

        % Pass the signals at the output of the receive antenna element to the receiver amplifier
        rxInt = step(vradar.Receiver,xcIradar);

        % Get the received signal without interference
        rxVictim = VictimRadar(vpaths,time);
        % Dechirp the received signal
        rxVsig = dechirp(rxVictim,sig);
        % Save sweep to data cube - create ADC data
        XcubeVradar(:,:,m) = rxVsig;

        % Dechirp the received signal + Interference and save sweep to data cube
        rx = dechirp(rxVictim+rxInt,sig);
        Xcube(:,:,m) = rx;

        % Get the current scenario time
        time = time + fmcwwav1.SweepTime;

        % Move targets forward in time for next sweep

    % Calculate the range-Doppler response 
    [Xrngdop,rnggrid,dopgrid] = rngdopresp(Xcube);

    % Plot Range-Doppler Map 
    surf(dopgrid, rnggrid,((20*log10(abs(squeeze(mean(Xrngdop,2)))))))
    zlabel('Power, dB'); xlabel('Velocity, m/s'); ylabel('Range, m')
    colormap(jet); colorbar; axis square; shading interp
    view([-10,55]); clim([-100 30]); axis([-120 120 0 100 -77 50])
    % Calculate the range-Doppler response with NO Interference and plot the RDM
    [XrngdopVradar,rnggrid,dopgrid] = rngdopresp(XcubeVradar);

    % Plot Range-Doppler Map 
    surf(dopgrid, rnggrid,((20*log10(abs(squeeze(mean(XrngdopVradar,2)))))))
    zlabel('Power, dB'); xlabel('Velocity, m/s'); ylabel('Range, m')
    title('RDM, No Interference','FontSize',12)
    colormap(jet); colorbar; axis square; shading interp
    view([-10,55]); clim([-100 30]); axis([-120 120 0 100  -77 50])

Figure contains an axes object. The axes object with title RDM contains an object of type surface.

Figure contains an axes object. The axes object with title RDM, No Interference contains an object of type surface.

The following animation shows the range-Doppler map for the victim radar for the duration of this scenario.


The following figure shows the interference effects for FMCW radars with different waveforms for the interfereing radar. In this example, the chirp time for the victim radar is considered to be 5μs, and the chirp time for the interfering radar is changed from 1.6μsto 5μs.



This example shows how to model the interference for automotive radars using Radar Toolbox. In this example, you learned how to define a radar model from a set of system requirements for a long-range radar and integrate the radar model with the Automated Driving Toolbox driving scenario simulation.


[1] Richards, Mark. Fundamentals of Radar Signal Processing. New York: McGraw Hill, 2005.

Helper Functions

generateFMCWWaveform Function

function fmcwwav = generateFMCWWaveform(fc,maxrange,rangeres,maxvel)
c = physconst('LightSpeed');                  % Speed of light in air (m/s)
lambda = freq2wavelen(fc,c);                  % Wavelength (m)

% Set the chirp duration to be 5 times the max range requirement
tm = 5*range2time(maxrange,c);                % Chirp duration (s)

% Determine the waveform bandwidth from the required range resolution
bw = rangeres2bw(rangeres,c);                 % Corresponding bandwidth (Hz)

% Set the sampling rate to satisfy both range and velocity requirements
sweepSlope = bw/tm;                           % FMCW sweep slope (Hz/s)
fbeatMax = range2beat(maxrange,sweepSlope,c); % Maximum beat frequency (Hz)
fdopMax = speed2dop(2*maxvel,lambda);         % Maximum Doppler shift (Hz)
fifMax = fbeatMax+fdopMax;                    % Maximum received IF (Hz)
fs = max(2*fifMax,bw);                        % Sampling rate (Hz)

% Configure the FMCW waveform using the waveform parameters derived from the requirements
fmcwwav = phased.FMCWWaveform('SweepTime',tm,'SweepBandwidth',bw,'SampleRate',fs,'SweepDirection','Up');

helperGeneratePaths Function

function [vpaths, ipaths] = helperGeneratePaths(tgtPoses,actProf,lambda)

tgtPos = reshape([zeros(1,3) tgtPoses.Position],3,[]);
tgtVel = reshape([zeros(1,3) tgtPoses.Velocity],3,[]);

% Position point targets at half of each target's height
tgtPos(3,:) = tgtPos(3,:)+0.5*[actProf.Height];
% Number of Targets
Ntargets = length(tgtPos(:,1))-1;

vpaths = repmat(struct(...
    'PathLength', zeros(1, 1), ...
    'PathLoss', zeros(1, 1), ...
    'ReflectionCoefficient', zeros(1,1), ...
    'AngleOfDeparture', zeros(2, 1), ...
    'AngleOfArrival', zeros(2, 1), ...
    'DopplerShift', zeros(1, 1)),...

for m = 1:Ntargets
    % Each target is already in the path length, angle
    [plength,tang] = rangeangle(tgtPos(:,m+1),tgtPos(:,1),eye(3));

    % path loss
    ploss = fspl(plength,lambda(1));

    % reflection gain
    rgain = aperture2gain(actProf(m+1).RCSPattern(1),lambda(1));

    % Doppler
    dop = speed2dop(2*radialspeed(tgtPos(:,m+1),tgtVel(:,m+1),tgtPos(:,1),tgtVel(:,1)),...

    % Target paths to victim radar
    vpaths(m).PathLength = 2*plength;
    vpaths(m).PathLoss = 2*ploss;
    vpaths(m).ReflectionCoefficient = db2mag(rgain);
    vpaths(m).AngleOfDeparture = tang;
    vpaths(m).AngleOfArrival = tang;
    vpaths(m).DopplerShift = dop;

% Interference paths to victim radar
ipaths = repmat(struct(...
    'PathLength', zeros(1, 1), ...
    'PathLoss', zeros(1, 1), ...
    'ReflectionCoefficient', zeros(1,1), ...
    'AngleOfDeparture', zeros(2, 1), ...
    'AngleOfArrival', zeros(2, 1), ...
    'DopplerShift', zeros(1, 1)),...

% Each target is already in the path length, angle
[plengthicar2vcar,tangicar2vcar] = rangeangle(tgtPos(:,1),tgtPos(:,2),eye(3));
[plengthicar2tgt,tangicar2tgt] = rangeangle(tgtPos(:,3),tgtPos(:,2),eye(3));
[plengthtgt2vcar,tangtgt2vcar] = rangeangle(tgtPos(:,1),tgtPos(:,3),eye(3));

% path loss
plossicar2vcar = fspl(plengthicar2vcar,lambda(2));  % from interfereing radar to victim radar
plossicar2tgt = fspl(plengthicar2tgt,lambda(2));    % from interfereing radar to target
plosstgt2vcar = fspl(plengthtgt2vcar,lambda(2));    % from target to victim radar

% reflection gain
rgainicar2vcar = aperture2gain(0.01,lambda(2));
rgainicar2tgt = aperture2gain(actProf(3).RCSPattern(1),lambda(2));

% Doppler
dopicar2vcar = speed2dop(2*radialspeed(tgtPos(:,1),tgtVel(:,1),tgtPos(:,2),tgtVel(:,2)),...

% Generate interference path from interfereing radar to victim radar
ipaths(1).PathLength = plengthicar2vcar;
ipaths(1).PathLoss = plossicar2vcar;
ipaths(1).ReflectionCoefficient = db2mag(rgainicar2vcar);
ipaths(1).AngleOfDeparture = tangicar2vcar;
ipaths(1).AngleOfArrival = tangicar2vcar;
ipaths(1).DopplerShift = dopicar2vcar;

% Generate interference path from interfereing radar to target and from target to victim radar
ipaths(2).PathLength = plengthicar2tgt+plengthtgt2vcar;
ipaths(2).PathLoss = plossicar2tgt+plosstgt2vcar;
ipaths(2).ReflectionCoefficient = db2mag(rgainicar2tgt);
ipaths(2).AngleOfDeparture = tangicar2tgt;
ipaths(2).AngleOfArrival = tangtgt2vcar;
ipaths(2).DopplerShift = dopicar2vcar;

helperAutoDrivingScenario Function

function [scenario, egoVehicle] = helperAutoDrivingScenario()

% Create the drivingScenario object and ego car
[scenario, egoVehicle] = createDrivingScenario;

% Restart the driving scenario to return the actors to their initial positions.

    function [scenario, egoVehicle] = createDrivingScenario

        % Construct a drivingScenario object.
        scenario = drivingScenario;

        % Add all road segments
        roadCenters = [83.3 4.6 0;
            -5.5 5 0];
        marking = [laneMarking('Unmarked')
            laneMarking('DoubleDashed', 'Color', [0.98 0.86 0.36])
        laneSpecification = lanespec(2, 'Width', 5, 'Marking', marking);
        road(scenario, roadCenters, 'Lanes', laneSpecification, 'Name', 'Road');

        % Add the ego vehicle
        egoVehicle = vehicle(scenario, ...
            'ClassID', 1, ...
            'Position', [6.8 3.3 0], ...
            'Mesh', driving.scenario.carMesh, ...
            'Name', 'Car');
        waypoints = [6.8 3.3 0;
            36.2 3.5 0];
        speed = [25;25];
        trajectory(egoVehicle, waypoints, speed);

        % Add the non-ego actors
        car1 = vehicle(scenario, ...
            'ClassID', 1, ...
            'Position', [54.8906703510737 7.64756738347315 0], ...
            'Mesh', driving.scenario.carMesh, ...
            'Name', 'Car1');
        waypoints = [54.8906703510737 7.64756738347315 0;
            -2.3 8.1 0];
        speed = [50;50];
        trajectory(car1, waypoints, speed);

        % Add the interfering Radar
        car2 = vehicle(scenario, ...
            'ClassID', 1, ...
            'Position', [28.7832282662372 2.52341165211024 0], ...
            'Mesh', driving.scenario.carMesh, ...
            'PlotColor', [0.494 0.184 0.556], ...
            'Name', 'Car2');
        waypoints = [28.7832282662372 2.52341165211024 0;
            82.1 1.9 0];
        speed = [50;50];
        trajectory(car2, waypoints, speed);