initcaukf

Create constant-acceleration unscented Kalman filter from detection report

collapse all in page

Syntax

filter = initcaukf(detection)

Description

example

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

Examples

collapse all

Open Live Script

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

Create the detection report from an initial 3-D measurement, (-200,-30,5), of the object position. Assume uncorrelated measurement noise.

detection = objectDetection(0,[-200;-30;5],'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 = initcaukf(detection)
filter = 
  trackingUKF with properties:

                          State: [9x1 double]
                StateCovariance: [9x9 double]

             StateTransitionFcn: @constacc
                   ProcessNoise: [3x3 double]
        HasAdditiveProcessNoise: 0

                 MeasurementFcn: @cameas
               MeasurementNoise: [3x3 double]
    HasAdditiveMeasurementNoise: 1

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

Show the state.

filter.State
ans = 9×1

  -200
     0
     0
   -30
     0
     0
     5
     0
     0

Show the state covariance matrix.

filter.StateCovariance
ans = 9×9

    2.0000         0         0         0         0         0         0         0         0
         0  100.0000         0         0         0         0         0         0         0
         0         0  100.0000         0         0         0         0         0         0
         0         0         0    2.0000         0         0         0         0         0
         0         0         0         0  100.0000         0         0         0         0
         0         0         0         0         0  100.0000         0         0         0
         0         0         0         0         0         0    2.0000         0         0
         0         0         0         0         0         0         0  100.0000         0
         0         0         0         0         0         0         0         0  100.0000
Open Live Script

Initialize a 3D constant-acceleration unscented Kalman filter from an initial detection report made from a 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∘, and the range to 1000 meters.

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

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

measparms = struct('Frame',frame,'OriginPosition',sensorpos, ...
    'OriginVelocity',sensorvel,'Orientation',laxes,'HasVelocity',false, ...
    'HasElevation',false);
meas = [45;1000];
measnoise = diag([3.0,2.0].^2);
detection = objectDetection(0,meas,'MeasurementNoise', ...
    measnoise,'MeasurementParameters',measparms)
detection = 
  objectDetection with properties:

                     Time: 0
              Measurement: [2x1 double]
         MeasurementNoise: [2x2 double]
              SensorIndex: 1
            ObjectClassID: 0
    MeasurementParameters: [1x1 struct]
         ObjectAttributes: {}


filter = initcaukf(detection);

Display the state vector.

disp(filter.State)
  732.1068
         0
         0
  667.1068
         0
         0
  -10.0000
         0
         0

Input Arguments

collapse all

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

collapse all

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 rate standard deviation of 1 m/s3.

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

Extended Capabilities

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

See Also

Functions

Objects

Introduced in R2018b

Sensor Fusion and Tracking Toolbox Documentation

Support