initctekf

Create constant turn-rate extended Kalman filter from detection report

Syntax

filter = initctekf(detection)

Description

example

filter = initctekf(detection) creates and initializes a constant-turn-rate extended Kalman filter from information contained in a detection report. For more information about the extended Kalman filter, see trackingEKF.

Examples

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Create and initialize a 2-D constant turn-rate extended Kalman filter object from an initial detection report.

Create the detection report from an initial 2-D measurement, (-250,-40), of the object position. Assume uncorrelated measurement noise.

Extend the measurement to three dimensions by adding a z-component of zero.

detection = objectDetection(0,[-250;-40;0],'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 = initctekf(detection)
filter = 
  trackingEKF with properties:

                          State: [7x1 double]
                StateCovariance: [7x7 double]

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

                 MeasurementFcn: @ctmeas
         MeasurementJacobianFcn: @ctmeasjac
               MeasurementNoise: [3x3 double]
    HasAdditiveMeasurementNoise: 1

Show the state.

filter.State
ans = 7×1

  -250
     0
   -40
     0
     0
     0
     0

Show the state covariance matrix.

filter.StateCovariance
ans = 7×7

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

Initialize a 2-D constant-turnrate extended Kalman filter from an initial detection report made from an initial 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 degrees, the range to 1000 meters, and the range rate to -4.0 m/s.

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

Create the measurement parameters structure. Set 'HasElevation' to false. Then, the measurement consists of azimuth, range, and range rate.

measparms = struct('Frame',frame,'OriginPosition',sensorpos, ...
    'OriginVelocity',sensorvel,'Orientation',laxes,'HasVelocity',true, ...
    'HasElevation',false);
meas = [45;1000;-4];
measnoise = diag([3.0,2,1.0].^2);
detection = objectDetection(0,meas,'MeasurementNoise', ...
    measnoise,'MeasurementParameters',measparms)
detection = 
  objectDetection with properties:

                     Time: 0
              Measurement: [3x1 double]
         MeasurementNoise: [3x3 double]
              SensorIndex: 1
            ObjectClassID: 0
    MeasurementParameters: [1x1 struct]
         ObjectAttributes: {}

filter = initctekf(detection);

Filter state vector.

disp(filter.State)
  732.1068
   -2.8284
  667.1068
    2.1716
         0
  -10.0000
         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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Extended Kalman filter, returned as a trackingEKF object.

Algorithms

  • The function computes the process noise matrix assuming a one-second time step. The function assumes an acceleration standard deviation of 1 m/s2, and a turn-rate acceleration standard deviation of 1°/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