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initcagmphd

Create constant acceleration gmphd filter

Description

phd = initcagmphd initializes a constant acceleration gmphd filter with zero components in the filter. By default, the data type of the filter is double.

example

phd = initcagmphd(detections) initializes a constant acceleration gmphd filter based on information provided in object detections, detections. The data type of the filter is the same as the data type of measurement in the detections.

The function initializes a constant acceleration state with the same convention as constacc and cameas, [x vx ax y vy ay z vz az].

Note

This initialization function is not compatible with trackerGNN, trackerJPDA, and trackerTOMHT System objects.

phd = initcagmphd(___,dataType) specifies the data type of the filter as single or double.

Examples

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Consider a point target located at [1;2;3]. Create detection for the target using objectDetection.

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

Initialize a constant acceleration gmphd filter using initcagmphd.

phd = initcagmphd(detection);

Illustrate the initial state and the extent setup of the phd filter.

state = phd.States
state = 9×1

     1
     0
     0
     2
     0
     0
     3
     0
     0

extent = phd.HasExtent
extent = logical
   0

Consider an extended object located at [1;2;3]. The object's detections are uniformly distributed in x-, y-, and z-directions with dimensions of 1.2, 2.3, and 3.5, respectively. Generate 20 randomly distributed detections for the object using objectDetection.

detections = cell(20,1);
location = [1;2;3];
dimensions = [1.2;2.3;3.5];
rng(2019);
measurements = location + dimensions.*(-1 + 2*rand(3,20));
for i = 1:20
    detections{i} = objectDetection(0,measurements(:,i)); 
end

Initialize a constant acceleration gmphd filter using initcagmphd.

phd = initcagmphd(detections);

The initial state of the filter is same as the mean of the measurements.

state = phd.States
state = 9×1

    1.1034
         0
         0
    2.5597
         0
         0
    2.4861
         0
         0

mean_measure = mean(measurements,2)
mean_measure = 3×1

    1.1034
    2.5597
    2.4861

By default, the function sets the HasExtent property to true if the number of measurements is greater than 1.

extent = phd.HasExtent
extent = logical
   1

Input Arguments

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Object detections, specified as a cell array of objectDetection objects. You can create detections directly, or you can obtain detections from the outputs of sensor objects, such as radarSensor, monostaticRadarSensor, irSensor, and sonarSensor.

Example: detection = objectDetection(0,[1;4.5;3],'MeasurementNoise',[1.0 0 0; 0 2.0 0; 0 0 1.5])

Data type of variables used in the filter, specified as "single" for single-precision and "Double" for double-precision. When specified as "single", the initialized filter does not use any double-precision variables.

Data Types: single | double

Output Arguments

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Gaussian mixture PHD filter, returned as a gmphd object.

Algorithms

  • You can use initcagmphd as the FilterInitializationFcn property of trackingSensorConfiguration.

  • When detections are provided as input, the function adds one component to the density, which reflects the mean of the detections. When the function is called without any inputs, a filter is initialized with no components in the density.

  • The function uses the spread of measurements to specify the positional covariance.

  • The function configures the process noise of the filter by assuming a unit standard deviation for the acceleration change rate.

  • The function specifies a maximum of 500 components in the filter.

  • The function sets the HasExtent property of the filter to true if the number of input detections are greater than one.

Extended Capabilities

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

Version History

Introduced in R2019b

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