initctukf

Create constant turn-rate unscented Kalman filter from detection report

Syntax

filter = initctukf(detection)

Description

filter = initctukf(detection) creates and initializes a constant-turn-rate unscented Kalman filter from information contained in a detection report. For more information about the unscented Kalman filter, see trackingUKF.

Note

initctukf represents velocity in the xy-plane with its Cartesian components, Vx and Vy. For the constant turn-rate and velocity magnitude motion model using velocity magnitude and course direction, see initctrvukf.

The function initializes a constant turn-rate state with the same convention as constturn and ctmeas, [x v_x y v_y ω z v_z], where ω is the turn-rate.

Examples

Initialize 2-D Constant Turn-Rate Unscented Kalman Filter

Create and initialize a 2-D constant turn-rate unscented Kalman filter object from an initial detection report.

Create the detection report from an initial 2D measurement, (-250,-40), of the object position. Assume uncorrelated measurement noise.

Extend the measurement to three dimensions by adding a z-component of zero.

detection = objectDetection(0,[-250;-40;0],'MeasurementNoise',2.0*eye(3), ...
    'SensorIndex',1,'ObjectClassID',1,'ObjectAttributes',{'Car',2});

Create the new filter from the detection report and display the filter properties.

filter = initctukf(detection)

filter = 
  trackingUKF with properties:

                          State: [7×1 double]
                StateCovariance: [7×7 double]

             StateTransitionFcn: @constturn
                   ProcessNoise: [4×4 double]
        HasAdditiveProcessNoise: 0

                 MeasurementFcn: @ctmeas
         HasMeasurementWrapping: 1
               MeasurementNoise: [3×3 double]
    HasAdditiveMeasurementNoise: 1

                          Alpha: 1.0000e-03
                           Beta: 2
                          Kappa: 0

                EnableSmoothing: 0

Show the filter state.

filter.State

ans = 7×1

  -250
     0
   -40
     0
     0
     0
     0

Show the state covariance matrix.

filter.StateCovariance

ans = 7×7

    2.0000         0         0         0         0         0         0
         0  100.0000         0         0         0         0         0
         0         0    2.0000         0         0         0         0
         0         0         0  100.0000         0         0         0
         0         0         0         0  100.0000         0         0
         0         0         0         0         0    2.0000         0
         0         0         0         0         0         0  100.0000

Create 2-D Constant Turn-rate UKF from Spherical Measurement

Initialize a 2-D constant turn-rate extended Kalman filter from an initial detection report made from an initial measurement in spherical coordinates. If you want to use spherical coordinates, then you must supply a measurement parameter structure as part of the detection report with the Frame field set to 'spherical'. Set the azimuth angle of the target to 45 degrees and the range to 1000 meters.

frame = 'spherical';
sensorpos = [25,-40,-10].';
sensorvel = [0;5;0];
laxes = eye(3);

Create the measurement parameters structure. Set 'HasVelocity' and 'HasElevation' to false. Then, the measurement consists of azimuth and range.

measparms = struct('Frame',frame,'OriginPosition',sensorpos, ...
    'OriginVelocity',sensorvel,'Orientation',laxes,'HasVelocity',false, ...
    'HasElevation',false);
meas = [45;1000];
measnoise = diag([3.0,2].^2);
detection = objectDetection(0,meas,'MeasurementNoise', ...
    measnoise,'MeasurementParameters',measparms)

detection = 
  objectDetection with properties:

                     Time: 0
              Measurement: [2×1 double]
         MeasurementNoise: [2×2 double]
              SensorIndex: 1
            ObjectClassID: 0
    ObjectClassParameters: []
    MeasurementParameters: [1×1 struct]
         ObjectAttributes: {}

filter = initctukf(detection);

Filter state vector.

disp(filter.State)

  732.1068
         0
  667.1068
         0
         0
  -10.0000
         0

Input Arguments

detection — Detection report

objectDetection object

Detection report, specified as an objectDetection object.

Example: detection = objectDetection(0,[1;4.5;3],'MeasurementNoise', [1.0 0 0; 0 2.0 0; 0 0 1.5])

Output Arguments

filter — Unscented Kalman filter

trackingUKF object

Unscented Kalman filter, returned as a trackingUKF object.

Algorithms

• The function computes the process noise matrix assuming a one-second time step. The function assumes an acceleration standard deviation of 1 m/s², and a turn-rate acceleration standard deviation of 1°/s².

• You can use this function as the FilterInitializationFcn property of a trackerGNN, trackerJPDA, or trackerTOMHT object.

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

Version History

Introduced in R2018b