initcvukf

Create constant-velocity unscented Kalman filter from detection report

Syntax

filter = initcvukf(detection)

Description

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

The function initializes a constant velocity state with the same convention as constvel and cvmeas, [x v_x y v_y z v_z].

Examples

Initialize 3-D Constant-Velocity Unscented Kalman Filter

Create and initialize a 3-D constant-velocity unscented Kalman filter object from an initial detection report.

Create the detection report from an initial 3-D measurement, (10,200,−5), of the object position.

detection = objectDetection(0,[10;200;-5],'MeasurementNoise',1.5*eye(3), ...
    'SensorIndex',1,'ObjectClassID',1,'ObjectAttributes',{'Sports Car',5});

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

filter = initcvukf(detection)

filter = 
  trackingUKF with properties:

                          State: [6×1 double]
                StateCovariance: [6×6 double]

             StateTransitionFcn: @constvel
                   ProcessNoise: [3×3 double]
        HasAdditiveProcessNoise: 0

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

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

                EnableSmoothing: 0

Display the state.

filter.State

ans = 6×1

    10
     0
   200
     0
    -5
     0

Show the state covariance.

filter.StateCovariance

ans = 6×6

    1.5000         0         0         0         0         0
         0  100.0000         0         0         0         0
         0         0    1.5000         0         0         0
         0         0         0  100.0000         0         0
         0         0         0         0    1.5000         0
         0         0         0         0         0  100.0000

Create Constant Velocity UKF from Spherical Measurement

Initialize a constant-velocity unscented Kalman filter from an initial detection report made from an initial measurement in spherical coordinates. Because the object lies in the x-y plane, no elevation measurement is made. 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, the range to 1000 meters, and the range rate to -4.0 m/s.

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

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

measparms = struct('Frame',frame,'OriginPosition',sensorpos, ...
    'OriginVelocity',sensorvel,'Orientation',laxes,'HasVelocity',true, ...
    'HasElevation',false);
meas = [45;1000;-4];
measnoise = diag([3.0,2,1.0].^2);
detection = objectDetection(0,meas,'MeasurementNoise', ...
    measnoise,'MeasurementParameters',measparms)

detection = 
  objectDetection with properties:

                     Time: 0
              Measurement: [3×1 double]
         MeasurementNoise: [3×3 double]
              SensorIndex: 1
            ObjectClassID: 0
    ObjectClassParameters: []
    MeasurementParameters: [1×1 struct]
         ObjectAttributes: {}

filter = initcvukf(detection);

Display filter state vector.

disp(filter.State)

  732.1068
   -2.8284
  667.1068
    2.1716
         0
         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 and an acceleration standard deviation of 1 m/s².

• You can use this function as the FilterInitializationFcn property of a multiObjectTracker object.

Extended Capabilities

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

Version History

Introduced in R2017a