curio-code / Radar-Simulation

A simulation environment is created in MATLAB using Automated Driving Toolbox and then vehicle tracking is done using Kalman filter utilizing simulated RADAR data for measurement update

Radar Simulation

Simulation Environment

1. Defining an empty scenario

scenario = drivingScenario;
scenario.SampleTime = 0.01;

2. Adding roads in into the environment

roadCenters = [0 0; 50 0; 100 0; 250 20; 500 40];
road(scenario, roadCenters, 'lanes',lanespec(2));

3. Creating Ego Vehicle and various traffic cars on the road

egoCar = vehicle(scenario, 'ClassID', 1);
trajectory(egoCar, roadCenters(2:end,:) - [0 1.8], 25); % On right lane

% Add a car in front of the ego vehicle
leadCar = vehicle(scenario, 'ClassID', 1);
trajectory(leadCar, [70 0; roadCenters(3:end,:)] - [0 1.8], 25); % On right lane

% Add a car that travels at 35 m/s along the road and passes the ego vehicle
passingCar = vehicle(scenario, 'ClassID', 1);
waypoints = [0 -1.8; 50 1.8; 100 1.8; 250 21.8; 400 32.2; 500 38.2];
trajectory(passingCar, waypoints, 35);

% Add a car behind the ego vehicle
chaseCar = vehicle(scenario, 'ClassID', 1);
trajectory(chaseCar, [25 0; roadCenters(2:end,:)] - [0 1.8], 25); % On right lane

4. Attaching 6 RADARs on the Ego Vehicle

sensors{1} = radarDetectionGenerator('SensorIndex', 1, 'Height', 0.2, 'MaxRange', 174, ...
    'SensorLocation', [egoCar.Wheelbase + egoCar.FrontOverhang, 0], 'FieldOfView', [20, 5]);

Create a Kalman filter for tracking

H = [1 0 0 0; 0 0 1 0; 0 1 0 0; 0 0 0 1];
filter = trackingKF('MotionModel', '2D Constant Velocity', 'State', H'*detection.Measurement,
'MeasurementModel', H, 'StateCovariance', H'*detection.MeasurementNoise*H, 'MeasurementNoise',
detection.MeasurementNoise);

Cummulative flow of the code