initcvgmphd

Create constant velocity gmphd filter

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Syntax

phd = initcvgmphd
phd = initcvgmphd(detections)

Description

phd = initcvgmphd initializes a constant velocity gmphd filter with zero components in the filter.

example

phd = initcvgmphd(detections) initializes a constant velocity gmphd filter based on information provided in object detections, detections. The function initializes a constant velocity state with the same convention as constvel and cvmeas, [x; vx; y; vy; z; vz].

Note

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

Examples

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Open Live Script

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

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

Initialize a constant velocity gmphd filter using initcvgmphd.

phd = initcvgmphd(detection);

Display the initial state and the extent setup of the gmphd filter.

state = phd.States
state = 6×1

     1
     0
     2
     0
     3
     0


extent = phd.HasExtent
extent = logical
   0
Open Live Script

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 velocity gmphd filter using initcvgmphd.

phd = initcvgmphd(detections);

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

state = phd.States
state = 6×1

    1.1034
         0
    2.5597
         0
    2.4861
         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 larger 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])

        
Output Arguments
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Gaussian mixture PHD filter, returned as a gmphd object.

        
Algorithms

      


  • You can use initcvgmphd 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
            acceleration standard deviation.
  • 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™.
See Also
 |  |  | 
Introduced in R2019b

    




























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