initcaukf

Create constant-acceleration unscented Kalman filter from detection report

Syntax

filter = initcaukf(detection)

Description

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.

The function initializes a constant acceleration state with the same convention as constacc and cameas, [x v_x a_x y v_y a_y z v_z a_z].

Examples

Initialize 3-D Constant-Acceleration Unscented Kalman Filter

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: [9×1 double]
                StateCovariance: [9×9 double]

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

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

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

                EnableSmoothing: 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

Create 3D Constant Acceleration UKF from Spherical Measurement

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 $$45^{\circ }$$, 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: [2×1 double]
         MeasurementNoise: [2×2 double]
              SensorIndex: 1
            ObjectClassID: 0
    ObjectClassParameters: []
    MeasurementParameters: [1×1 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

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 rate standard deviation of 1 m/s³.

• 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