initcaukf

Create constant-acceleration unscented Kalman filter from detection report

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

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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     0     0     0     0     0     0     0     0
     0   100     0     0     0     0     0     0     0
     0     0   100     0     0     0     0     0     0
     0     0     0     2     0     0     0     0     0
     0     0     0     0   100     0     0     0     0
     0     0     0     0     0   100     0     0     0
     0     0     0     0     0     0     2     0     0
     0     0     0     0     0     0     0   100     0
     0     0     0     0     0     0     0     0   100

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

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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

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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™.

Introduced in R2018b