initcvkf

Create constant-velocity linear Kalman filter from detection report

Syntax

filter = initcvkf(detection)

Description

example

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

Examples

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Create and initialize a 2-D linear Kalman filter object from an initial detection report.

Create the detection report from an initial 2-D measurement, (10,20), of the object position.

detection = objectDetection(0,[10;20],'MeasurementNoise',[1 0.2; 0.2 2], ...
    'SensorIndex',1,'ObjectClassID',1,'ObjectAttributes',{'Yellow Car',5});

Create the new track from the detection report.

filter = initcvkf(detection)
filter = 
  trackingKF with properties:

               State: [4x1 double]
     StateCovariance: [4x4 double]

         MotionModel: '2D Constant Velocity'
        ControlModel: []
        ProcessNoise: [4x4 double]

    MeasurementModel: [2x4 double]
    MeasurementNoise: [2x2 double]

Show the state.

filter.State
ans = 4×1

    10
     0
    20
     0

Show the state transition model.

filter.StateTransitionModel
ans = 4×4

     1     1     0     0
     0     1     0     0
     0     0     1     1
     0     0     0     1

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

Create the detection report from an initial 3-D measurement, (10,20,−5), of the object position.

detection = objectDetection(0,[10;20;-5],'MeasurementNoise',eye(3), ...
    'SensorIndex', 1,'ObjectClassID',1,'ObjectAttributes',{'Green Car', 5});

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

filter = initcvkf(detection)
filter = 
  trackingKF with properties:

               State: [6x1 double]
     StateCovariance: [6x6 double]

         MotionModel: '3D Constant Velocity'
        ControlModel: []
        ProcessNoise: [6x6 double]

    MeasurementModel: [3x6 double]
    MeasurementNoise: [3x3 double]

Show the state.

filter.State
ans = 6×1

    10
     0
    20
     0
    -5
     0

Show the state transition model.

filter.StateTransitionModel
ans = 6×6

     1     1     0     0     0     0
     0     1     0     0     0     0
     0     0     1     1     0     0
     0     0     0     1     0     0
     0     0     0     0     1     1
     0     0     0     0     0     1

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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Linear Kalman filter, returned as a trackingKF 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 trackerGNN or trackerTOMHT object.

Extended Capabilities

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

Introduced in R2018b