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initctpf

Create constant turn-rate tracking particle filter from detection report

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Syntax

pf = initctpf(detection)

Description

pf = initctpf(detection) initializes a constant turn-rate particle filter for object tracking based on information provided in an objectDetection object, detection.

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

example

Examples

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Open Live Script

Create a constant turn rate tracking particle filter object, trackingPF, from an initial detection report. The detection report is made from an initial 3-D position measurement of the particle filter state in rectangular coordinates. You can obtain the 3-D position measurement using the constant turn rate measurement function, ctmeas.

This example uses the coordinates, x = 1, y = 3, z = 0 and a 3-D position measurement noise of [1 0.2 0; 0.2 2 0; 0 0 1]. 

detection = objectDetection(0, [1;3;0], 'MeasurementNoise', [1 0.2 0; 0.2 2 0; 0 0 1]);

Use initctpf to create a trackingPF filter initialized at the provided position and using the measurement noise defined above.

pf = initctpf(detection)
pf = 
  trackingPF with properties:

                       State: [7⨯1 double]
             StateCovariance: [7⨯7 double]
     IsStateVariableCircular: [0 0 0 0 0 0 0]

          StateTransitionFcn: @constturn
     ProcessNoiseSamplingFcn: []
                ProcessNoise: [4x4 double]
     HasAdditiveProcessNoise: 0

              MeasurementFcn: @ctmeas
    MeasurementLikelihoodFcn: []
            MeasurementNoise: [3x3 double]

                   Particles: [7x1000 double]
                     Weights: [1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 ... ] (1x1000 double)
            ResamplingPolicy: [1x1 trackingResamplingPolicy]
            ResamplingMethod: 'multinomial'

Check the values of the state and the measurement noise. Verify that the filter state, pf.State, has approximately the same position components as the detection measurement, detection.Measurement.

pf.State
ans = 7×1

    0.9674
    0.3690
    3.0471
    0.2733
    0.3056
   -0.0590
    0.0382

Verify that the filter measurement noise, pf.MeasurementNoise, is the same as the detection.MeasurementNoise values. 

pf.MeasurementNoise
ans = 3×3

    1.0000    0.2000         0
    0.2000    2.0000         0
         0         0    1.0000
Open Live Script

Create a constant turn rate tracking particle filter object, trackingPF, from an initial detection report. The detection report is made from an initial 3-D position measurement of the particle filter state in spherical coordinates. You can obtain the 3D position measurement using the constant turn rate measurement function, ctmeas.

This example uses the coordinates, az = 30, e1 = 5, r = 100, rr = 4 and a measurement noise of diag([2.5, 2.5, 0.5, 1].^2). 

meas = [30;5;100;4];
measNoise = diag([2.5, 2.5, 0.5, 1].^2);

Use the MeasurementParameters property of the detection object to define the frame. When not defined, the fields of the MeasurementParameters struct use default values. In this example, sensor position, sensor velocity, orientation, elevation, and range rate flags are default.

measParams = struct('Frame','spherical');
detection = objectDetection(0,meas,'MeasurementNoise',measNoise,...
    'MeasurementParameters',measParams) 
detection = 
  objectDetection with properties:

                     Time: 0
              Measurement: [4x1 double]
         MeasurementNoise: [4x4 double]
              SensorIndex: 1
            ObjectClassID: 0
    ObjectClassParameters: []
    MeasurementParameters: [1x1 struct]
         ObjectAttributes: {}

Use initctpf to create a trackingPF filter initialized at the provided position and using the measurement noise defined above.

pf = initctpf(detection)
pf = 
  trackingPF with properties:

                       State: [7⨯1 double]
             StateCovariance: [7⨯7 double]
     IsStateVariableCircular: [0 0 0 0 0 0 0]

          StateTransitionFcn: @constturn
     ProcessNoiseSamplingFcn: []
                ProcessNoise: [4x4 double]
     HasAdditiveProcessNoise: 0

              MeasurementFcn: @ctmeas
    MeasurementLikelihoodFcn: []
            MeasurementNoise: [4x4 double]

                   Particles: [7x1000 double]
                     Weights: [1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 ... ] (1x1000 double)
            ResamplingPolicy: [1x1 trackingResamplingPolicy]
            ResamplingMethod: 'multinomial'

Verify that the filter state produces approximately the same measurement as detection.Measurement.

meas2 = ctmeas(pf.State, detection.MeasurementParameters)
meas2 = 4×1

   29.9188
    5.0976
   99.8303
    4.0255

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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Constant turn-rate particle filter for object tracking, returned as a trackingPF object.

Algorithms

  • The function configures the filter with 1000 particles. In creating the filter, the function computes the process noise matrix assuming a unit acceleration standard deviation and a unit angular acceleration standard deviation.

  • You can use this function as the FilterInitializationFcn property of trackerTOMHT and trackerGNN System objects.

Extended Capabilities

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

Version History

Introduced in R2018b

See Also

Functions

Objects