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Represent sensor configuration for tracking


The trackingSensorConfiguration object creates the configuration for a sensor used with a trackerPHD System object™. It allows you to specify the sensor parameters such as clutter density, sensor limits, sensor resolution. You can also specify how a tracker perceives the detections from the sensor using properties such as FilterInitializationFcn, SensorTransformFcn, and SensorTransformParameters. See Create a Tracking Sensor Configuration for more details. The trackingSensorConfiguration object enables the tracker to perform three main routine operations:

  • Evaluate the probability of detection at points in state-space.

  • Initiate components in the probability hypothesis density.

  • Obtain the clutter density of the sensor.



config = trackingSensorConfiguration(SensorIndex)
config = trackingSensorConfiguration(SensorIndex,Name,Value)


config = trackingSensorConfiguration(SensorIndex) creates a trackingSensorConfiguration object with a specified sensor index, SensorIndex, and default property values.


config = trackingSensorConfiguration(SensorIndex,Name,Value) allows you to set properties using one or more name-value pairs.


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Unique sensor identifier, specified as a positive integer. This property distinguishes detections that come from different sensors in a multi-sensor system. When creating a trackingSensorConfiguration object, you must specify the SensorIndex as the first input argument in the creation syntax.

Example: 2

Data Types: double

Indicate the detection reporting status of the sensor, specified as false or true. Set this property to true when the sensor must report detections within its sensor limits to the tracker. If a track or target was supposed to be detected by a sensor but the sensor reported no detections, then this information is used to count against the probability of existence of the track when the isValidTime property is set to true.

Data Types: logical

Filter initialization function, specified as a function handle or as a character vector containing the name of a valid filter initialization function. The function initializes the PHD filter used by trackerPHD. The function must support the following syntaxes:

filter = filterInitializationFcn()
filter = filterInitializationFcn(detections)
filter is a valid PHD filter with components for new-born targets, and detections is a cell array of objectDetection. The first syntax allows you to specify the predictive birth density in the PHD filter without using detections. The second syntax allows the filter to initialize the adaptive birth density using detection information. See the BirthRate property of trackerPHD for more details. If you create your own FilterInitilizationFcn, you must also provide a transform function using the SensorTransformFcn property. Other than the default filter initialization function initcvggiwphd, you can also use initctggiwphd and initcaggiwphd in Sensor Fusion and Tracking Toolbox™.

Data Types: function_handle | char

Sensor transform function, specified as a function handle or as a character vector containing the name of a valid sensor transform function. The function transforms a track's state into the sensor's detection state. For example, the function transforms the track's state in the scenario Cartesian frame to the sensor's spherical frame. You can create your own sensor transform function, but it must support the following syntax:

detStates = SensorTransformFcn(trackStates,params)
params are the parameters stored in the SensorTransformParameters property. Notice that the signature of the function is similar to a measurement function. Therefore, you can use a measurement function (such as cvmeas, ctmeas, or cameas) as the SensorTransformFcn.

Note that the default SensorTransformFcn is the sensor transform function of the filter returned by FilterInitilizationFcn. For example, the initicvggiwphd function returns the default cvmeas, whereas initictggiwphd and initicaggiwphd functions return ctmeas and cameas, respectively.

Data Types: function_handle | char

Parameters for the sensor transform function, returned as a structure or an array of structures. If you only need to transform the state once, specify it as a structure. If you need to transform the state n times, specify it as an n-by-1 array of structures. For example, to transform a state from the scenario frame to the sensor frame, you usually need to first transform the state from the scenario rectangular frame to the platform rectangular frame, and then transform the state from the platform rectangular frame to the sensor spherical frame. The fields of the structure are:


Child coordinate frame type, specified as 'Rectangular' or 'Spherical'.


Child frame origin position expressed in the Parent frame, specified as a 3-by-1 vector.


Child frame origin velocity expressed in the parent frame, specified as a 3-by-1 vector.


Relative orientation between frames, specified as a 3-by-3 rotation matrix. If the IsParentToChild property is set to false, then specify Orientation as the rotation from the child frame to the parent frame. If the IsParentToChild property is set to true, then specify Orientation as the rotation from the parent frame to the child frame.


Flag to indicate the direction of rotation between parent and child frame, specified as true or false. The default is false. See description of the Orientation field for details.


Indicates whether outputs contain azimuth components, specified as true or false.


Indicates whether outputs contain elevation components, specified as true or false.


Indicates whether outputs contain range components, specified as true or false.


Indicates whether outputs contains velocity components, specified as true or false.

Note that here the scenario frame is the parent frame of the platform frame, and the platform frame is the parent frame of the sensor frame.

The default values for SensorTransformParameters are a 2-by-1 array of structures as:

FieldsStruct 1Struct 2

In this table, Struct 2 accounts for the transformation from the scenario rectangular frame to the platform rectangular frame, and Struct 1 accounts for the transformation from the platform rectangular frame to the sensor spherical frame, given the isParentToChild property is set to false.

Data Types: struct

Sensor's detection limits, specified as an N-by-2 matrix, where N is the output dimension of the sensor transform function. The matrix must describe the lower and upper detection limits of the sensor in the same order as the outputs of the sensor transform function.

If you use cvmeas, cameas, or ctmeas as the sensor transform function, then you need to provide the sensor limits in order as:

The description of these limits and their default values are given in the following table. Note that the default values for SensorLimits are a 3-by-2 matrix including the top six elements in the table. Moreover, if you use these three functions, you can specify the matrix to be in other sizes (1-by-2, 2-by-2, or 3-by-4), but you have to specify these limits in the sequence shown in the SensorLimits matrix.

LimitsDescriptionDefault values

Minimum detectable azimuth in degrees.


Maximum detectable azimuth in degrees.


Minimum detectable elevation in degrees.


Maximum detectable elevation in degrees.


Minimum detectable range in meters.


Maximum detectable range in meters.


Minimum detectable range rate in meters per second.


Maximum detectable range rate in meters per second.


Data Types: double

Resolution of a sensor, specified as a N-element positive-valued vector, where N is the number of parameters specified in the SensorLimits property. If you want to assign only one resolution cell for a parameter, simply specify its resolution as the difference between the maximum limit and the minimum limit of the parameter.

Data Types: double

Maximum number of detections the sensor can report per object, specified as a positive integer.

Example: 3

Data Types: double

Expected number of false alarms per unit volume from the sensor, specified as a positive scalar.

Example: 2e-3

Data Types: double

Probability of detecting a target estimated to be outside of the sensor limits, specified as a positive scalar. This property allows a trackerPHD object to consider that the estimated target, which is outside the sensor limits, may be detectable.

Example: 0.03

Data Types: double


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Consider a radar with the following sensor limits and sensor resolution.

  azLimits = [-10 10];
  elLimits = [-2.5 2.5];
  rangeLimits = [0 500];
  rangeRateLimits = [-50 50];
  sensorLimits = [azLimits;elLimits;rangeLimits;rangeRateLimits];
  sensorResolution = [5 2 10 3];

Specifying the sensor transform function that transforms the Cartesian coordinates [x;y;vx;vy] in the scenario frame to the spherical coordinates [az;el;range;rr] in the sensor's frame. You can use the measurement function cvmeas as the sensor transform function.

  transformFcn = @cvmeas;

To specify the parameters required for cvmeas, use the SensorTransformParameters property. Here, you assume the sensor is mounted at the center of the platform and the platform located at [100;30;20] is moving with a velocity of [-5;4;2] units per second in the scenario frame.

The first structure defines the sensor's location, velocity, and orientation in the platform frame.

  params(1) = struct('Frame','Spherical','OriginPosition',[0;0;0],...

The second structure defines the platform's location, velocity, and orientation in the scenario frame.

  params(2) = struct('Frame','Rectangular','OriginPosition',[100;30;20],...

Create the configuration.

  config = trackingSensorConfiguration('SensorIndex',3,'SensorLimits',sensorLimits,...
config = 

  trackingSensorConfiguration with properties:

                  SensorIndex: 3
                  IsValidTime: 0

                 SensorLimits: [4x2 double]
             SensorResolution: [4x1 double]
           SensorTransformFcn: @cvmeas
    SensorTransformParameters: [1x2 struct]

      FilterInitializationFcn: @initcvggiwphd
          MaxNumDetsPerObject: Inf

               ClutterDensity: 1.0000e-03
         DetectionProbability: 0.9000
      MinDetectionProbability: 0.0500

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