trackingSensorConfiguration

Represent sensor configuration for tracking

Description

The trackingSensorConfiguration object creates the configuration for a sensor used with a trackerPHD System object™ or a trackerGridRFS System object. You can use the trackingSensorConfiguration object to specify sensor parameters such as clutter density, sensor limits, and 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.

When used with a trackerPHD System object, the trackingSensorConfiguration object enables the tracker to perform four main routine operations:

Evaluate the probability of detection at points in state-space.
Compute the expected number of detections from a target.
Initiate components in the probability hypothesis density.
Obtain the clutter density of the sensor.

When used with a trackerGridRFS System object, the trackingSensorConfiguration object assists the tracker to project sensor data on 2-D grid. The tracker uses the SensorTransformParameters property to calculate the location and orientation of the sensor in the tracking coordinate frame. The tracker uses the SensorLimits property to calculate the field of view and the maximum range of the sensor. The SensorTransformFcn and FilterInitializationFcn properties are not relevant for the trackerGridRFS System object.

Creation

Syntax

config = trackingSensorConfiguration(sensorIndex)

config = trackingSensorConfiguration(sensor)

config = trackingSensorConfiguration(sensor,platformPose)

config = trackingSensorConfiguration(sensorBlock)

config = trackingSensorConfiguration(sensorBlock,platformPose)

configs = trackingSensorConfiguration(platform)

configs = trackingSensorConfiguration(scenario)

___ = trackingSensorConfiguration(___,Name,Value)

Description

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

example

config = trackingSensorConfiguration(sensor) returns a trackingSensorConfiguration object based on a sensor object.

example

config = trackingSensorConfiguration(sensor,platformPose) additionally specifies the pose of the sensor mounting platform. In this case, the SensorTransformParameters property includes the coordinate transform information from the scenario frame to the sensor platform frame.

example

config = trackingSensorConfiguration(sensorBlock) returns a trackingSensorConfiguration object based on a sensor block in Simulink.

example

config = trackingSensorConfiguration(sensorBlock,platformPose) specifies the pose of the sensor mounting platform. In this case, the SensorTransformParameters property includes the coordinate transform information from the scenario frame to the sensor platform frame.

configs = trackingSensorConfiguration(platform) returns the sensor configurations for all sensors mounted on a platform in a tracking scenario.

example

configs = trackingSensorConfiguration(scenario) returns the sensor configurations for all sensors defined in a tracking scenario. The object uses the poses of the platforms in the scenario to define the SensorTransformParameters property of each returned configuration.

example

___ = trackingSensorConfiguration(___,Name,Value) set properties using one or more name-value pairs. Use this syntax with any of the previous syntaxes.

example

Inputs

expand all

`sensorIndex` — Sensor Index
positive integer

Unique sensor index, specified as a positive integer.

`sensor` — Sensor object
sensor object

Sensor object, specified as one of these objects:

fusionRadarSensor
radarDataGenerator (Radar Toolbox)
drivingRadarDataGenerator (Automated Driving Toolbox)
monostaticLidarSensor
irSensor
sonarSensor
lidarPointCloudGenerator (Automated Driving Toolbox)
visionDetectionGenerator (Automated Driving Toolbox)

`platformPose` — Platform pose information
structure

Platform pose information, specified as a structure. The structure has these fields.

Field Name	Description
Position	Position of the platform with respect to the scenario frame, specified as a three-element vector.
Velocity	Velocity of the platform with respect to the scenario frame, specified as a three-element vector.
Orientation	Orientation of the platform frame with respect to the scenario frame, specified as a 3-by-3 rotation matrix or a `quaternion`.

Alternately, you can specify the structure using these fields.

Field Name	Description
Position	Position of the platform with respect to the scenario frame, specified as a three-element vector.
Velocity	Velocity of the platform with respect to the scenario frame, specified as a three-element vector.
Yaw	Yaw angle of the platform frame with respect to the scenario frame, specified as a scalar in degrees. The yaw angle corresponds to the z-axis rotation.
Pitch	Pitch angle of the platform frame with respect to the scenario frame, specified as a scalar in degrees. The pitch angle corresponds to the y-axis rotation.
Roll	Roll angle of the platform frame with respect to the scenario frame, specified as a scalar in degrees. The roll angle corresponds to the x-axis rotation.

`sensorBlock` — Simulink sensor block
handle of a valid Simulink sensor block | path of a valid Simulink sensor block

Simulink sensor block, specified as the handle or the path of a valid Simulink sensor block. A valid sensor block is one of those Simulink blocks:

Fusion Radar Sensor
Driving Radar Data Generator (Automated Driving Toolbox)
Radar Data Generator (Radar Toolbox)
Vision Detection Generator (Automated Driving Toolbox)
Lidar Point Cloud Generator (Automated Driving Toolbox)

`platform` — Platform
`Platform` object

Platform, specified as a Platform object.

`scenario` — Tracking scenario
`trackingScenrio` object

Tracking scenario, specified as a trackingScenario object.

Outputs

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`config` — Tracking sensor configuration
`trackingSensorConfiguration` object

Tracking sensor configuration, returned as a trackingSensorConfiguration object.

`configs` — Tracking sensor configurations
N-element cell-array of `trackingSensorConfiguration` objects

Tracking sensor configurations, returned as an N-element cell-array of trackingSensorConfiguration objects. N is the number of sensors on the platform or in the scenario.

Properties

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`SensorIndex` — Unique sensor identifier
positive integer

Unique sensor identifier, specified as a positive integer. This property distinguishes data that come from different sensors in a multi-sensor system.

Note

If you specify the platform or scenario input argument, the object ignores the name-value input argument for this property.

Example: 2

Data Types: double

`IsValidTime` — Indicate detection reporting status
`false` (default) | `true`

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

`FilterInitializationFcn` — Filter initialization function
`@initcvggiwphd` (default) | function handle | string scalar

Filter initialization function, specified as a function handle or as a string scalar 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 objects. 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 FilterInitializationFcn, you must also provide a transform function using the SensorTransformFcn property. Other than the default filter initialization function initcvggiwphd, Sensor Fusion and Tracking Toolbox™ also provides other initialization functions such as initctrectgmphd, initctgmphd, initcvgmphd, initcagmphd, initctggiwphd and initcaggiwphd.

Data Types: function_handle | char

`SensorTransformFcn` — Sensor transform function
`@cvmeas` | function handle | character vector

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 this 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.

Depending on the filter type and the target type, the output detStates needs to return differently.

When you use the object with gmphd for non-extended targets or with ggiwphd, detStates is a N-by-M matrix, where N is the number of rows in the SensorLimits property and M is the number of input states in trackStates.
When you used the object with gmphd for extended targets, the SensorTransformFcn allows you to specify multiple detStates per trackState. In this case, detStates is a N-by-M-by-S matrix, where S is the number of detectable sources on the extended target. For example, if the target is described by a rectangular state, the detectable sources can be the corners of the rectangle.
If any of the sources falls inside the SensorLimits, the target is declared detectable. The functions uses the spread (maximum value − minimum value) of each detStates and the ratio between the spread and sensor resolution on each sensor limit to calculate the expected number of detections from each extended target. You can override this default setting by providing an optional output in the SensorTransformFcn as:
```
[..., Nexp] = SensorTransformFcn(trackStates, params)
```
where Nexp is the expected number of detections from each extended track state.

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

`SensorTransformParameters` — Parameters for sensor transform function
structure | array of structures

Parameters for the sensor transform function, returned as a structure or an array of structures. If you need to transform the state only once, specify it as a structure. If you need to transform the state multiple 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 structure contains these fields.

Field	Description
`Frame`	Child coordinate frame type, specified as `'Rectangular'` or `'Spherical'`.
`OriginPosition`	Child frame origin position expressed in the Parent frame, specified as a 3-by-1 vector.
`OriginVelocity`	Child frame origin velocity expressed in the parent frame, specified as a 3-by-1 vector.
`Orientation`	Relative orientation between frames, specified as a 3-by-3 rotation matrix. If you set the `IsParentToChild` property to `false`, then specify `Orientation` as the rotation from the child frame to the parent frame. If you set the `IsParentToChild` property to `true`, then specify `Orientation` as the rotation from the parent frame to the child frame.
`IsParentToChild`	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.
`HasAzimuth`	Indicates whether outputs contain azimuth components, specified as `true` or `false`.
`HasElevation`	Indicates whether outputs contain elevation components, specified as `true` or `false`.
`HasRange`	Indicates whether outputs contain range components, specified as `true` or `false`.
`HasVelocity`	Indicates whether outputs contains velocity components, specified as `true` or `false`.

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.

Fields	Struct 1	Struct 2
Frame	`'Spherical'`	`'Rectangular'`
OriginPosition	`[0;0;0]`	`[0;0;0]`
OriginVelocity	`[0;0;0]`	`[0;0;0]`
Orientation	`eye(3)`	`eye(3)`
IsParentToChild	`false`	`false`
HasAzimuth	`true`	`true`
HasElevation	`true`	`true`
HasRange	`true`	`true`
HasVelocity	`false`	`true`

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 that you set the IsParentToChild property to false.

Note

If you use a custom sensor transformation function in the SensorTransformFcn property, you can specify this property in any format as long as the sensor transformation function accepts it.

Note

If you specify the platform or scenario input argument, the object ignores the name-value input argument for this property.

Data Types: struct

`SensorLimits` — Sensor's detection limits
3-by-2 matrix (default) | N-by-2 matrix

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:

$SensorLimits = [\begin{matrix} minAz & maxAz \\ minEl & maxEl \\ minRng & maxRng \\ minRr & maxRr \end{matrix}]$

The description of the limits and their default values are given in this table. 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 a different size matrix (1-by-2, 2-by-2, or 3-by-4), but you have to specify the limits in the sequence in the SensorLimits matrix.

Limits	Description	Default values
minAz	Minimum detectable azimuth in degrees.	`-10`
maxAz	Maximum detectable azimuth in degrees.	`10`
minEl	Minimum detectable elevation in degrees.	`-2.5`
maxEl	Maximum detectable elevation in degrees.	`2.5`
minRng	Minimum detectable range in meters.	`0`
maxRng	Maximum detectable range in meters.	`1000`
minRr	Minimum detectable range rate in meters per second.	N/A
maxRr	Maximum detectable range rate in meters per second.	N/A

Note

If you specify the platform or scenario input argument, the object ignores the name-value input argument for this property.

Data Types: double

`SensorResolution` — Resolution of sensor
`[4;2;10]` (default) | N-element positive-valued vector

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.

Note

If you specify the platform or scenario input argument, the object ignores the name-value input argument for this property.

Data Types: double

`MaxNumDetections` — Maximum number of detections
`Inf` (default) | positive integer

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

Note

If you specify the platform or scenario input argument, the object ignores the name-value input argument for this property.

Example: 1

Data Types: double

`MaxNumDetsPerObject` — Maximum number of detections per object
`Inf` (default) | positive integer

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

Note

If you specify the platform or scenario input argument, the object ignores the name-value input argument for this property.

Example: 3

Data Types: double

`ClutterDensity` — Expected number of false alarms per unit volume
`1e-3` (default) | positive scalar

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

Note

If you specify the platform or scenario input argument, the object ignores the name-value input argument for this property.

Example: 2e-3

Data Types: double

`DetectionProbability` — Probability of detecting target inside the coverage limits
`0.9` (default) | scalar in the range (0,1]

Probability of detecting a target inside the coverage limits, specified as a scalar in the range (0, 1].

Example: 0.75

Data Types: single | double

`MinDetectionProbability` — Probability of detecting track estimated to be outside of sensor limits
`0.05` (default) | positive scalar

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, can be detectable.

Note

If you specify the platform or scenario input argument, the object ignores the name-value input argument for this property.

Example: 0.03

Data Types: double

Examples

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Create Radar Sensor Configuration

Open Live Script

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];

Specify the sensor transform function that transforms the Cartesian coordinates [x;y;vx;vy] in the scenario frame to spherical coordinates [az;el;range;rr] in the sensor's frame. 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], ...
      "OriginVelocity",[0;0;0], ...
      "Orientation",eye(3), ...
      "HasRange",true, ...
      "HasVelocity",true);

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

  params(2) = struct("Frame","Rectangular", ...
      "OriginPosition",[100;30;20], ...
      "OriginVelocity",[-5;4;2], ...
      "Orientation",eye(3), ...
      "HasRange",true, ...
      "HasVelocity",true);

Create the configuration.

  config = trackingSensorConfiguration(SensorIndex=3,SensorLimits=sensorLimits,...
                                       SensorResolution=sensorResolution,...
                                       SensorTransformParameters=params,...
                                       SensorTransformFcn=@cvmeas,...
                                       FilterInitializationFcn=@initcvggiwphd)

config = 
  trackingSensorConfiguration with properties:

                  SensorIndex: 3
                  IsValidTime: 0

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

      FilterInitializationFcn: @initcvggiwphd
             MaxNumDetections: Inf
          MaxNumDetsPerObject: Inf

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

Create Tracking Sensor Configuration for `fusionRadarSensor`

Open Live Script

Create a fusionRadarSensor object and specify its properties.

sensor = fusionRadarSensor(1, ...
    FieldOfView=[20 5], ...
    RangeLimits=[0 500], ...
    HasRangeRate=true, ...
    HasElevation=true, ...
    RangeRateLimits=[-50 50], ...
    AzimuthResolution=5, ...
    RangeResolution=10, ...
    ElevationResolution=2, ...
    RangeRateResolution=3);

Specify the cvmeas function as the sensor transform function.

transformFcn = @cvmeas;

Create a trackingSensorConfiguration object.

config = trackingSensorConfiguration(sensor,SensorTransformFcn=transformFcn)

config = 
  trackingSensorConfiguration with properties:

                  SensorIndex: 1
                  IsValidTime: 0

                 SensorLimits: [4x2 double]
             SensorResolution: [4x1 double]
           SensorTransformFcn: @cvmeas
    SensorTransformParameters: [2x1 struct]

      FilterInitializationFcn: []
             MaxNumDetections: Inf
          MaxNumDetsPerObject: 1

               ClutterDensity: 1.0485e-07
         DetectionProbability: 0.9000
      MinDetectionProbability: 0.0500

Create `trackingSensorConfiguration` Using Sensor and Platform Pose Inputs

Open Live Script

Create a monostatic lidar sensor object.

sensor = monostaticLidarSensor(1);

Define the pose of the sensor platform with respect to the scenario frame.

platformPose = struct("Position", [10 -10 0], ...
    "Velocity", [1 1 0], ...
    "Orientation", eye(3));

Create a trackingSensorConfiguration object based on the sensor and the platform pose input.

config = trackingSensorConfiguration(sensor,platformPose)

config = 
  trackingSensorConfiguration with properties:

                  SensorIndex: 1
                  IsValidTime: 0

                 SensorLimits: [3x2 double]
             SensorResolution: [3x1 double]
           SensorTransformFcn: []
    SensorTransformParameters: [2x1 struct]

      FilterInitializationFcn: []
             MaxNumDetections: Inf
          MaxNumDetsPerObject: Inf

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

Create `trackingSensorConfiguration` Using Platform Input

Open Live Script

Create a trackingScenario object and add a platform.

scene = trackingScenario;
plat = platform(scene);

Add two sensors to the platform.

plat.Sensors = {fusionRadarSensor(1);monostaticLidarSensor(2)};

Create trackingSensorConfiguration objects using the platform.

configs = trackingSensorConfiguration(plat)

configs=2×1 cell array
    {1x1 trackingSensorConfiguration}
    {1x1 trackingSensorConfiguration}

Create `trackingSensorConfiguration` from Simulink Block

This example uses:

Open Live Script

Open a saved Simulink model that contains a Fusion Radar Sensor block.

open_system("sensorModel");

Get the path and handle of the block.

blockPath = getfullname(gcb);
blockHandle = getSimulinkBlockHandle(blockPath);

Create a trackingSensorConfiguration object based on the block path.

tscByBlockPath = trackingSensorConfiguration(blockPath)

tscByBlockPath = 
  trackingSensorConfiguration with properties:

                  SensorIndex: 1
                  IsValidTime: 0

                 SensorLimits: [2x2 double]
             SensorResolution: [2x1 double]
           SensorTransformFcn: []
    SensorTransformParameters: [2x1 struct]

      FilterInitializationFcn: []
             MaxNumDetections: 100
          MaxNumDetsPerObject: 1

               ClutterDensity: 5.2536e-13
         DetectionProbability: 0.9000
      MinDetectionProbability: 0.0500

Create a trackingSensorConfiguration object based on the block handle.

tscByBlockHandle = trackingSensorConfiguration(blockHandle)

tscByBlockHandle = 
  trackingSensorConfiguration with properties:

                  SensorIndex: 1
                  IsValidTime: 0

                 SensorLimits: [2x2 double]
             SensorResolution: [2x1 double]
           SensorTransformFcn: []
    SensorTransformParameters: [2x1 struct]

      FilterInitializationFcn: []
             MaxNumDetections: 100
          MaxNumDetsPerObject: 1

               ClutterDensity: 5.2536e-13
         DetectionProbability: 0.9000
      MinDetectionProbability: 0.0500

Create `trackingSensorConfiguration` Using Scenario Input

Open Live Script

Create a trackingScenario object and add two platforms.

scene = trackingScenario;
plat1 = platform(scene);
plat2 = platform(scene);

Add two sensors to the first platform and one sensor to the second platform.

plat1.Sensors = {fusionRadarSensor(1);monostaticLidarSensor(2)};
plat2.Sensors = {fusionRadarSensor(3)};

Create trackingSensorConfiguration objects using the scenario.

configs = trackingSensorConfiguration(scene)

configs=3×1 cell array
    {1x1 trackingSensorConfiguration}
    {1x1 trackingSensorConfiguration}
    {1x1 trackingSensorConfiguration}

More About

expand all

Create a Tracking Sensor Configuration

To create the configuration for a sensor, you first need to specify the sensor transform function, which is usually given as:

$Y = g (x, p)$

where x denotes the tracking state, Y denotes detection states, and p denotes the required parameters. For object tracking applications, you mainly focus on obtaining an object's tracking state. For example, a radar sensor can measure an object's azimuth, elevation, range, and possibly range-rate. Using a trackingSensorConfiguration object, you can specify a radar's transform function using the SensorTransformFcn property and specify the radar's mounting location, orientation, and velocity using corresponding fields in the SensorTransformParameters property. If the object is moving at a constant velocity, constant acceleration, or constant turning, you can use the built-in measurement function – cvmeas, cameas, or ctmeas, respectively – as the SensorTransformFcn. To set up the exact outputs of these three functions, specify the hasAzimuth, hasElevation, hasRange, and hasVelocity fields as true or false in the SensorTransformParameters property.

To set up the configuration of a sensor, you also need to specify the sensor's detection ability. Primarily, you need to specify the sensor's detection limits. For all the outputs of the sensor transform function, you need to provide the detection limits in the same order of these outputs using the SensorLimits property. For example, for a radar sensor, you might need to provide its azimuth, elevation, range, and range-rate limits. You can also specify the radar's SensorResolution and MaxNumDetsPerObject properties if you want to consider extended object detection. You might also want to specify other properties, such as ClutterDensity, IsValidTime, and MinDetectionProbability to further clarify the sensor's detection ability.

Extended Capabilities

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

Version History

Introduced in R2019a

expand all

R2022b: Create tracking sensor configuration from sensor, platform, and tracking scenario

You can now create trackingSensorConfiguration objects, which are required by the trackerPHD and trackerGridRFS System objects, from sensor, platform, and tracking scenario. Specifically, you can:

Generate a trackingSensorConfiguration object directly based on one of these sensor objects:
- monostaticLidarSensor
- irSensor
- sonarSensor
- lidarPointCloudGenerator (Automated Driving Toolbox)
- visionDetectionGenerator (Automated Driving Toolbox)
Previously, you could directly generate a trackingSensorConfiguration object from the fusionRadarSensor, radarDataGenerator (Radar Toolbox), or drivingRadarDataGenerator (Automated Driving Toolbox) object.
Generate a trackingSensorConfiguration object based on one of these Simulink blocks:
- Fusion Radar Sensor
- Driving Radar Data Generator (Automated Driving Toolbox)
- Radar Data Generator (Radar Toolbox)
- Vision Detection Generator (Automated Driving Toolbox)
- Lidar Point Cloud Generator (Automated Driving Toolbox)
Generate trackingSensorConfigurarion objects for all sensors mounted on a Platform object.
Specify a platform pose input representing the coordinate transformation from the scenario to the sensor frame when you create the trackingSensorConfigurarion object.
Generate trackingSensorConfigurarion objects for all sensors in a trackingScenario object.

The trackingSensorConfiguration has a new property, MaxNumDetections. Use the new property to set the maximum number of detections by a specific sensor.

trackingSensorConfiguration

Description

Creation

Syntax

Description

Inputs

sensorIndex — Sensor Index positive integer

sensor — Sensor object sensor object

platformPose — Platform pose information structure

sensorBlock — Simulink sensor block handle of a valid Simulink sensor block | path of a valid Simulink sensor block

platform — Platform Platform object

scenario — Tracking scenario trackingScenrio object

Outputs

config — Tracking sensor configuration trackingSensorConfiguration object

configs — Tracking sensor configurations N-element cell-array of trackingSensorConfiguration objects

Properties

SensorIndex — Unique sensor identifier positive integer

IsValidTime — Indicate detection reporting status false (default) | true

FilterInitializationFcn — Filter initialization function @initcvggiwphd (default) | function handle | string scalar

SensorTransformFcn — Sensor transform function @cvmeas | function handle | character vector

SensorTransformParameters — Parameters for sensor transform function structure | array of structures

SensorLimits — Sensor's detection limits 3-by-2 matrix (default) | N-by-2 matrix

SensorResolution — Resolution of sensor [4;2;10] (default) | N-element positive-valued vector

MaxNumDetections — Maximum number of detections Inf (default) | positive integer

MaxNumDetsPerObject — Maximum number of detections per object Inf (default) | positive integer

ClutterDensity — Expected number of false alarms per unit volume 1e-3 (default) | positive scalar

DetectionProbability — Probability of detecting target inside the coverage limits 0.9 (default) | scalar in the range (0,1]

MinDetectionProbability — Probability of detecting track estimated to be outside of sensor limits 0.05 (default) | positive scalar

Examples

Create Radar Sensor Configuration

Create Tracking Sensor Configuration for fusionRadarSensor

Create trackingSensorConfiguration Using Sensor and Platform Pose Inputs

Create trackingSensorConfiguration Using Platform Input

Create trackingSensorConfiguration from Simulink Block

Create trackingSensorConfiguration Using Scenario Input

More About

Create a Tracking Sensor Configuration

Extended Capabilities

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

Version History

R2022b: Create tracking sensor configuration from sensor, platform, and tracking scenario

See Also

`sensorIndex` — Sensor Index
positive integer

`sensor` — Sensor object
sensor object

`platformPose` — Platform pose information
structure

`sensorBlock` — Simulink sensor block
handle of a valid Simulink sensor block | path of a valid Simulink sensor block

`platform` — Platform
`Platform` object

`scenario` — Tracking scenario
`trackingScenrio` object

`config` — Tracking sensor configuration
`trackingSensorConfiguration` object

`configs` — Tracking sensor configurations
N-element cell-array of `trackingSensorConfiguration` objects

`SensorIndex` — Unique sensor identifier
positive integer

`IsValidTime` — Indicate detection reporting status
`false` (default) | `true`

`FilterInitializationFcn` — Filter initialization function
`@initcvggiwphd` (default) | function handle | string scalar

`SensorTransformFcn` — Sensor transform function
`@cvmeas` | function handle | character vector

`SensorTransformParameters` — Parameters for sensor transform function
structure | array of structures

`SensorLimits` — Sensor's detection limits
3-by-2 matrix (default) | N-by-2 matrix

`SensorResolution` — Resolution of sensor
`[4;2;10]` (default) | N-element positive-valued vector

`MaxNumDetections` — Maximum number of detections
`Inf` (default) | positive integer

`MaxNumDetsPerObject` — Maximum number of detections per object
`Inf` (default) | positive integer

`ClutterDensity` — Expected number of false alarms per unit volume
`1e-3` (default) | positive scalar

`DetectionProbability` — Probability of detecting target inside the coverage limits
`0.9` (default) | scalar in the range (0,1]

`MinDetectionProbability` — Probability of detecting track estimated to be outside of sensor limits
`0.05` (default) | positive scalar

Create Tracking Sensor Configuration for `fusionRadarSensor`

Create `trackingSensorConfiguration` Using Sensor and Platform Pose Inputs

Create `trackingSensorConfiguration` Using Platform Input

Create `trackingSensorConfiguration` from Simulink Block

Create `trackingSensorConfiguration` Using Scenario Input

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