Grid-Based Multi Object Tracker

Grid-based multi-object tracker using random finite set approach

Since R2021b

• 

Libraries:
Sensor Fusion and Tracking Toolbox / Multi-Object Tracking Algorithms

Description

The Grid-Based Multi Object Tracker is a tracker capable of processing detections of multiple targets from multiple sensors in a 2-D environment. The tracker tracks dynamic objects around an autonomous system using high resolution sensor data such as point clouds and radar detections. The tracker uses the random finite set (RFS) based approach, combined with Dempster-Shafer approximations [1], to estimate the dynamic characteristics of the grid cells. To extract objects from the grid, the tracker uses a cell-to-track association scheme [2]. For more details, see Algorithms.

Examples

Grid-based Tracking in Urban Environments Using Multiple Lidars in Simulink

Track moving objects with multiple lidars using a grid-based tracker in Simulink. You use the Grid-Based Multi Object Tracker Simulink block to define the grid-based tracker. This Grid-based tracker uses dynamic occupancy grid map as an intermediate representation of the environment. This example closely follows the Grid-Based Tracking in Urban Environments Using Multiple Lidars MATLAB® example.

• Since R2021b
• Open Model

Ports

Input

Sensor Data — Sensor data

Simulink^® bus containing MATLAB^® structure

Sensor data, specified as a Simulink bus containing a MATLAB structure. The structure contains these fields:

Field	Description
NumSensors	Number of sensors, specified as a nonnegative integer.
SensorData	Sensor data, specified as an array of sensor data structures. The first NumSensors elements of the array are actual detections.

Each SensorData structure contains these fields::

Field	Description
Time	Measurement time, specified as a nonnegative scalar.
SensorIndex	Unique identifier of the sensor, specified as a positive integer.
Measurement	Object measurements, specified a K-by-M matrix. K is the dimension of each measurement and M is the number of measurements. Each measurement defines the positional aspects of the detection in a rectangular or spherical frame.
MeasurementParameters	Measurement parameters, specified as a structure describing the transformation from the particle state to the measurement. See the MeasurementParameter property of the objectDetection object for details on setting up this field.
NumMeasurements	Number of measurements or number of points in the point cloud sensor detections, specified as a positive integer.

Note

The Time field of each structure must be less than or equal to the time of the current invocation of the block. The time must also be greater than the update time specified in the previous invocation of the block.

Prediction Time — Track update time

real scalar

Track update time, specified as a real scalar in seconds. The tracker updates all tracks to this time. The update time must increase with each invocation of the block, and must be at least as large as the largest Time specified to the Sensor Data input port.

If this port is not enabled, the simulation clock managed by Simulink determines the update time.

Dependencies

To enable this port, in the Port Setting tab, set the Prediction time source parameter to Input port.

Sensor Configurations — Configurations of tracking sensors

Simulink bus containing MATLAB structure

Configurations of the tracking sensors, specified as a Simulink bus containing a MATLAB structure. The structure contains these fields:

Field	Description
NumConfigurations	Number of sensor configurations, specified as a positive integer.
Configurations	Sensor configurations, specified as an array of sensor configuration structures. The first NumConfigurations elements of the array are actual configurations. The field names and definitions must correspond to names and values, respectively, of properties of the trackingSensorConfiguration object. The tracker ignores the FilterInitializationFcn, SensorTransformFcn, and MaxNumDetsPerObject fields, even when you specify them in the structure. If you use a Radar Data Generator (Radar Toolbox) block in the tracking system, you can directly specify this value by using the Configuration output of the Radar Data Generator (Radar Toolbox) block, instead.

Dependencies

To enable this port, in the Tracker Configuration tab, select the Update sensor configurations with time parameter.

State Parameters — Track state parameters

Simulink bus containing MATLAB structure

Track state parameters, specified as a Simulink bus containing a MATLAB structure. The block uses this structure as the value of the StateParameters field of the generated tracks. You can use these parameters to define the reference frame in which the block report tracks as well as other desirable attributes of the generated tracks.

For example, you can use a structure with these fields to define a rectangular reference frame with origin position at [10 10 0] meters and origin velocity of [2 -2 0] meters per second, with respect to the scenario frame.

Field Name	Value
Frame	"Rectangular"
Position	[10 10 0]
Velocity	[2 -2 0]

Dependencies

To enable this port, in the Tracker Configuration tab, select the Update track state parameters with time parameter.

Output

Confirmed Tracks — Confirmed tracks

Simulink bus containing MATLAB structure

Confirmed tracks updated to the current time, returned as a Simulink bus containing a MATLAB structure. The structure contains these fields:

Field	Description
NumTracks	Number of tracks.
Tracks	Array of track structures of a length set by the Maximum number of tracks parameter. Only the first NumTracks element of the array are actual tracks.

The fields of the track structure are shown in Track Structure. The state form of each track follows the form specified in the Motion model for tracking parameter.

Tentative Tracks — Tentative tracks

Simulink bus containing MATLAB structure

Tentative tracks updated to the current time, returned as a Simulink bus containing a MATLAB structure. A track is tentative before it is confirmed.

The structure contains these fields:

Field	Description
NumTracks	Number of tracks
Tracks	Array of track structures of a length set by the Maximum number of tracks parameter. Only the first NumTracks elements of the array are actual tracks.

The fields of the track structure are shown in Track Structure. The state form of each track follows the form specified in the Motion model for tracking parameter.

Dependencies

To enable this port, in the Port Setting tab, select Enable tentative tracks output.

All Tracks — Confirmed and Tentative tracks

Simulink bus containing MATLAB structure

Combined list of confirmed and tentative tracks updated to the current time, returned as a Simulink bus containing a MATLAB structure.

The structure contains these fields:

Field	Description
NumTracks	Number of tracks
Tracks	Array of track structures of a length set by the Maximum number of tracks parameter. Only the first NumTracks elements of the array are actual tracks.

The fields of the track structure are shown in Track Structure. The state form of each track follows the form specified in the Motion model for tracking parameter.

Dependencies

To enable this port, in the Port Setting tab, select Enable all tracks output.

Parameters

Tracker Configuration

Tracker identifier — Unique tracker identifier

0 (default) | nonnegative integer

Specify the unique tracker identifier as a nonnegative integer. This parameter is passed as the SourceIndex in the tracker outputs, and distinguishes tracks that come from different trackers in a multiple-tracker system. You must specify this property as a positive integer to use the track outputs as inputs to a Track-To-Track Fuser block.

Example: 1

Maximum number of tracks — Maximum number of tracks

100 (default) | positive integer

Maximum number of tracks that the block can maintain, specified as a positive integer.

Maximum number of sensors — Maximum number of sensors

20 (default) | positive integer

Specify the maximum number of sensors that can be connected to the tracker as a positive integer. MaxNumSensors must be greater than or equal to the largest value of SensorIndex found in all the sensor data used to update the block.

Data Types: single | double

Sensor configurations — Configurations of tracking sensors

struct('SensorIndex',1,'IsValidTime',true,'SensorLimits',[-180 180; 0 100]) (default) | structure | structure array

Specify the configurations of tracking sensors as a structure or an array of structures. This parameter provides the tracking sensor configuration information, such as sensor detection limits and sensor resolution, to the tracker. You must specify these fields for each structure:

Field Name	Format
SensorIndex	Unique identifier of the sensor, specified as a positive integer.
IsValidTime	Indicate if the sensor data should be used to update tracks, specified as true or false.
SensorTransformParameters	Parameters of the sensor transform function, specified as a p-element array of measurement parameter structures. p is the number of sensors. Each structure should contain fields with the same names as the measurement parameters used in a measurement function, such as the cvmeas function. The first structure must describe the transformation from the autonomous system to the sensor coordinates. The subsequent structure describes the transformation from the autonomous system to the tracking coordinate frame. If you only provide one structure, the tracker assumes tracking is performed in the coordinate frame of the autonomous system.
SensorLimits	Sensor detection limits, specified as a 2-by-2 matrix. The first row specifies the lower and upper limits of the azimuth angle in degrees. The second row specifies the lower and upper limits of the detection range in meters.

The other allowed field names of each structure correspond to the property names of the trackingSensorConfiguration object. The tracker ignores the FilterInitializationFcn, SensorTransformFcn, and MaxNumDetsPerObject fields, even when you specify them in the structure.

You can update the configurations by using the Sensor configurations input port after selecting the Update sensor configurations with time parameter.

Update sensor configurations with time — Update sensor configurations with time

off (default) | on

Select this parameter to enable the Sensor Configurations input port. This enables you to update tracking sensor configurations during the simulation.

Track state parameters — Parameters of track state reference frame

structure

Specify the parameters of the track state reference frame as a structure. The block passes the value of this parameter to the StateParameters field of the generated tracks. You can use these parameters to define the reference frame in which the block reports tracks, as well as other desirable attributes of the generated tracks.

For example, you can use a structure with these fields to define a rectangular reference frame with origin position at [10 10 0] meters and origin velocity of [2 -2 0] meters per second with respect to the scenario frame.

Field Name	Value
Frame	"Rectangular"
Position	[10 10 0]
Velocity	[2 -2 0]

You can update the track state parameters through the State Parameters input port by selecting the Update track state parameters with time parameter.

Data Types: struct

Update track state parameters with time — Update track state parameters with time

off (default) | on

Select this parameter to enable the input port for track state parameters through the State Parameters input port.

Enable GPU computation — Enable GPU computation

off (default) | on

Select this parameter to enable using GPU for the estimation of the dynamic grid map. Enabling GPU computation requires the Parallel Computing Toolbox™.

X-dimension of grid — X-dimension of grid

100 (default) | positive scalar

Specify the x-dimension of the grid in the local coordinates as a positive scalar, in meters.

Y-dimension of grid — Y-dimension of grid

100 (default) | positive scalar

Specify the y-dimension of the grid in the local coordinates as a positive scalar, in meters.

Resolution of grid — Resolution of grid

1 (default) | positive scalar

Specify the resolution of the grid as a positive scalar. The resolution represents the number of cells per meter of the grid for both the x- and y-directions of the grid.

Grid origin — Origin of grid

[-50 -50] (default) | positive scalar

Specify the origin of the grid in the local coordinate frame as a two-element vector of scalars, in meters.

Simulate using — Type of simulation to run

Interpreted Execution (default) | Code Generation

Select the type of simulation to run from these options:

• Interpreted execution — Simulate the model using the MATLAB interpreter. This option shortens startup time. In the Interpreted execution mode, you can debug the source code of the block.

• Code generation — Simulate the model using generated C code. The first time you run a simulation, Simulink generates C code for the block. The C code is reused for subsequent simulations as long as the model does not change. This option requires additional startup time.

Particle Filtering

Motion model for tracking — Motion model for tracking

constant-velocity (default) | constant-acceleration | constant-turnrate

Select the motion model for tracking as constant-velocity, constant-acceleration, or constant-turnrate.

The particle state and object state for each motion model are:

Motion Model	Particle State	Object State
constant-velocity	[x; vx; y; vy]	[x; vx; y; vy; yaw; L; W]
constant-acceleration	[x; vx; ax; y; vy; ay]	[x; vx; ax; y; vy; ay; yaw; L; W]
constant-turn-rate	[x; vx; y; vy; w]	[x; vx; y; vy; w; yaw; L; W]

where:

• x — Position of the object in the x-direction of the local tracking frame (m)

• y — Position of the object in the y-direction of the local tracking frame (m)

• vx — Velocity of the object in the x-direction of the local tracking frame (m/s)

• vy — Velocity of the object in the y-direction of the local tracking frame (m/s)

• ax — Acceleration of the object in the x-direction of the local tracking frame (m/s²)

• ay — Acceleration of the object in the y-direction of the local tracking frame (m/s²)

• w — Yaw-rate of the object in the local tracking frame (deg/s)

• yaw — Yaw angle of the object in the local tracking frame (deg)

• L — Length of the object (m)

• W — Width of the object (m)

Minimum and maximum velocity of objects (m/s) — Minimum and maximum velocities of objects (m/s)

[-10 10; -10 10] (default) | 2-by-2 matrix of scalar

Specify the minimum and maximum velocities of objects as a 2-by-2 matrix of scalars in m/s. The first row specifies the lower and upper velocity limits in the x-direction, and the second row specifies the lower and upper velocity limits in the y-direction. The tracker uses these limits to sample new particles in the grid using a uniform distribution.

Enable additive process noise — Enable additive process noise

false (default) | true

Select this parameter to model process noise as additive. When selected, process noise is added directly to the state vector. Otherwise, noise is incorporated in the motion model.

Process noise covariance matrix — Process noise covariance matrix

eye(2) (default) | N-by-N matrix

Specify process noise covariance as an N-by-N matrix. This parameter specifies the process noise for positions of particles and the geometric centers of targets.

• If you select the Enable additive process noise parameter, the process noise adds directly to the prediction model. In this case, N is equal to the dimension of the particle state in the Motion model for tracking parameter.

• If you do not select the Enable additive process noise parameter, define the process noise according to the selected motion model. The model adds process noise to the higher-order terms, such as acceleration for the constant-velocity model.

  MotionModel	Number of Terms for Acceleration	Meaning of Terms
  constant-velocity	2	Acceleration in the x- and y- directions
  constant-acceleration	2	Jerk in the x- and y- directions
  constant-turn-rate	3	Acceleration in the x- and y- directions, as well as the angular acceleration

Example: [1.0 0.05; 0.05 2]

Number of persistent particles — Number of persistent particles

1e5 (default) | positive integer

Specify the number of persistent particles per grid as a positive integer. A higher number of particles can improve estimation quality, but can increase computational cost.

Number of new-born particles per step — Number of new-born particles per step

1e4 (default) | positive scalar

Specify the number of new-born (initialized) particles per time step as a positive integer. The tracker determines the locations of these new-born particles by using the mismatch between the predicted and the updated occupancy belief masses and the Probability of birth in a cell per step parameter. A reasonable value of this parameter is approximately 10 percent of the number of particles specified by the Number of persistent particles parameter.

Probability of birth in a cell per step — Probability of birth in a cell per step

0.01 (default) | scalar in range [0, 1)

Specify the probability of target birth in a cell per step as a scalar in the range [0, 1). The birth probability controls the probability that new particles are generated in a cell.

Example: 1e-4

Death rate of targets per unit time — Death rate of targets per unit time

1e-3 (default) | positive scalar

Specify the death rate of targets per unit time as a positive scalar. Death rate indicates the possibility that a particle or target vanishes after each time step. Death rate (P_d) influences the survival probability (P_s) of a component across successive time steps as:

$$P_{\text{s}}={\left(1-P_d\right)}^{\Delta T}$$

where ΔT is the number of time steps.

Example: 1e-4

Confidence in free space prediction — Confidence in free space prediction

0.8 (default) | scalar

Specify the confidence in free space prediction as a scalar. In the prediction stage of the tracker, this parameter reduces the belief mass of a cell to be in the "free" (unoccupied) state as:

$$m_{k|k-1}(F)={\alpha }^{\Delta T}{m}_{k-1}(F)$$

where k is the time step index, m is the belief mass, α is the factor representing the confidence in free space prediction, and ΔT is the time step.

Random number generation — Method of random number seed generation

Repeatable (default) | Not repeatable | Specify seed

Select the method of random number seed generation as Repeatable, Not repeatable, or Specify seed.

• Repeatable — The block uses the same random seed every time.

• Not repeatable — The block uses a different random seed every time.

• Specify seed — Specify a random seed for the block using the Initial Seed parameter.

Initial Seed — Initial seed for randomization

0 (default) | nonnegative integer

Specify the initial seed for randomization in the block as a nonnegative integer.

Dependencies

To enable this parameter, select the Random number generation parameter as Specify seed.

Track Management

Clustering method for new object extraction — Clustering method for new object extraction

DBSCAN (default) | Custom

Specify the clustering method used for new object extraction as DBSCAN or Custom.

• DBSCAN — Cluster unassigned dynamic grid cells using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. You can configure the DBSCAN algorithm by specifying the Threshold for DBSCAN clustering and Minimum number of cells per cluster for DBSCAN parameters.

• Custom — Cluster unassigned dynamic grid cells using a custom clustering function specified in the Name of 'Custom' clustering function parameter.

Threshold for DBSCAN clustering — Threshold for DBSCAN clustering

5 (default) | positive scalar

Specify the threshold for DBSCAN clustering as a positive scalar.

Dependencies

To enable this parameter, select the Clustering method for new object extraction parameter as DBSCAN.

Minimum number of cells per cluster for DBSCAN — Minimum number of cells per cluster for DBSCAN

2 (default) | positive integer

Specify the minimum number of cells per cluster for DBSCAN as a positive integer. This parameter affects whether a point is a core point in the DBSCAN algorithm.

Dependencies

To enable this parameter, select the Clustering method for new object extraction parameter as DBSCAN.

Name of 'Custom' clustering function — Name of custom clustering function

function name

Specify the name of the custom function for clustering unassigned grid cells as a function name. The function must support this signature:

function indices = myFunction(dynamicGridCells)

where dynamicGridCells is a structure that defines a set of grid cells initializing the track. It must have these fields:

Field	Description
Width	Width of the cell, specified as a positive scalar.
GridIndices	Indices of the grid cells, specified as an N-by-2 array, where N is the number of unassigned cells. The first element specifies the grid index in the x-direction and the second element specifies the grid index in the y-direction.
State	States of the grid cells, specified as a P-by-N array of scalars, where P is the dimension of the state and N is the number of unassigned cells.
StateCovariance	State covariances of the grid cells, specified as a P-by-P-by-N array of scalars, where P is the dimension of the state and N is the number of unassigned cells.
OccupancyMass	Occupancy belief mass of the cells, specified as an N-element vector, where N is the number of unassigned cells.
FreeMass	Free belief mass of the cells, specified as an N-element vector, where N is the number of unassigned cells.

The custom function must return indices as an N-element vector of indices that defines the cluster index for each dynamic grid cell.

Dependencies

To enable this parameter, select the Clustering method for new object extraction parameter as Custom.

Function to initialize tracks from grid cell sets — Function to initialize tracks from grid cell sets

trackerGridRFS.defaultTrackInitialization (default) | function name

Specify the function used to initialize new tracks as a function name. The initialization function initiates a track from a set of dynamic grid cells.

The default initialization function merges the Gaussian estimates from cells to describe the state of the object. The orientation of the object aligns with the direction of its mean velocity. With a defined orientation, the length and width of the object are extracted using the geometric properties of the cells. The block calculates uncertainties in length, width, and orientation estimates using linear approximations.

If you choose to use your own initialization function, the function must support this signature:

function track = myFunction(dynamicGridCells)

where dynamicGridCells is a structure that defines a set of grid cells initializing the track. It has these fields:

Field	Description
Width	Width of the cell, specified as a positive scalar.
GridIndices	Indices of the grid cells, specified as an N-by-2 array, where N is the number of unassigned cells. The first element specifies the grid index in the x-direction and the second element specifies the grid index in the y-direction.
State	States of the grid cells, specified as a P-by-N array of scalars, where P is the dimension of the state and N is the number of unassigned cells.
StateCovariance	State covariances of the grid cells, specified as a P-by-P-by-N array of scalars, where P is the dimension of the state and N is the number of unassigned cells.
OccupancyMass	Occupancy belief mass of the cells, specified as an N-element vector, where N is the number of unassigned cells.
FreeMass	Free belief mass of the cells, specified as an N-element vector, where N is the number of unassigned cells.

The custom function must return track as an objectTrack object or a structure with field names that correspond to the property names of an objectTrack object. The dimension of the state must be the same as the state dimension specified in the Motion model for tracking parameter.

Example: myFunction

Function to update existing tracks — Function to update existing tracks

trackerGridRFS.defaultTrackUpdate (default) | function name

Specify the function used to update an existing track using its associated set of dynamic grid cells as a function name.

The default update function updates the State and StateCovariance fields of the track using the new estimate from the dynamic grid cells associated with the track. The update process is similar to the initialization process for the Function to initialize tracks from grid cell sets parameter. The tracker does not apply filtering to the state and state covariance.

If you choose to customize your own update function, the function must support this signature:

function updatedTrack = TrackUpdateFcn(predictedTrack,dynamicGridCells)

where:

• predictedTrack is the predicted track of an object, specified as an objectTrack object.

• dynamicGridCells is a structure that defines a set of dynamic grid cells associated with the track. The structure has these fields:

  Field	Description
  Width	Width of the cell, specified as a positive scalar.
  GridIndices	Indices of the grid cells, specified as an N-by-2 array, where N is the number of unassigned cells. The first element specifies the grid index in the x-direction and the second element specifies the grid index in the y-direction.
  State	States of the grid cells, specified as a P-by-N array of scalars, where P is the dimension of the state and N is the number of unassigned cells.
  StateCovariance	State covariances of the grid cells, specified as a P-by-P-by-N array of scalars, where P is the dimension of the state and N is the number of unassigned cells.
  OccupancyMass	Occupancy belief mass of the cells, specified as an N-element vector, where N is the number of unassigned cells.
  FreeMass	Free belief mass of the cells, specified as an N-element vector, where N is the number of unassigned cells.

• updatedTrack is the updated track, returned as an objectTrack object or a structure that has field names that correspond to the property names of an objectTrack object.

Example: TrackUpdateFcn

Threshold for assigning dynamic grid cells to tracks — Threshold for assigning dynamic grid cells to tracks

30 (default) | positive scalar

Specify the threshold for assigning dynamic grid cells to tracks as a positive scalar. A dynamic grid cell can only be associated to a track if its distance (represented by the negative log-likelihood) to the track is less than the value of this parameter.

• Increase the threshold if a dynamic cell is not being assigned to a track that it should be assigned to.

• Decrease the threshold if there are dynamic cells being assigned to a track that they should be not assigned to.

Example: 18.1

Confirmation threshold [M N] — Threshold for track confirmation

[2 3] (default) | 1-by-2 vector of positive integers

Specify the threshold for track confirmation as a 1-by-2 vector of positive integers [M N]. A track is confirmed if it has been assigned to dynamic grid cells in at least M updates of the last N updates.

Deletion threshold [P Q] — Threshold for track deletion

[5 5] (default) | 1-by-2 vector of positive integers

Specify the threshold for track deletion as a 1-by-2 vector of positive integers [P Q]. A track is deleted if has not been assigned to any dynamic grid cell in at least P updates of the last Q updates.

Example: 0.01

Data Types: single | double

Visualization

Enable dynamic grid map visualization — Enable dynamic grid map visualization

on (default) | off

Select this parameter to enable the dynamic grid map visualization. If a current axes exists, the block shows the dynamic map on the current axes. Otherwise, the block creates an axes to show the map.

Plot velocity — Enable velocity plotting

off (default) | on

Select this parameter to enable velocity plotting on the dynamic grid map.

Fast update — Enable updating from previous map

on (default) | off

Select this parameter to enable updating from previous map. When selected, the block plots the map via a lightweight update to the previous map in the figure. Otherwise, the block plots a new map on the figure every time.

Invert colors — Enable inverted colors

off (default) | on

Select this parameter to enable inverted colors on the map. When selected, the block plots empty space in white and occupied space in black. Otherwise, the block plots empty space in black and occupied space in white.

Port Setting

Prediction time source — Source of prediction time

Input port (default) | Auto

Specify the source for prediction time as Input port or Auto. Select Input port to input an update time by using the Prediction Time input port. Otherwise, the simulation clock managed by Simulink determines the update time.

Enable tentative tracks output — Enable output port for tentative tracks

off (default) | on

Select this parameter to enable the output of tentative tracks through the Tentative Tracks output port.

Enable all tracks output — Enable output port for all tracks

off (default) | on

Select this parameter to enable the output of all the tracks through the All Tracks output port.

Source of output bus name — Source of output track bus name

Auto (default) | Property

Source of the output track bus name, specified as:

• Auto — The block automatically creates an output track bus name.

• Property — Specify the output track bus name by using the Specify an output bus name parameter.

Algorithms

Tracker Logic Flow

The Grid-Based Multi Object Tracker block initializes, confirms, and deletes tracks automatically by using this algorithm:

1. The tracker projects sensor data from all sensors on a two-dimensional grid map to represent the occupancy and free evidence in a Dempster-Shafer framework.

2. The tracker uses a particle-based approach to estimate the dynamic state of the 2-D grid. This helps the tracker classify each cell as dynamic or static.

3. The tracker manage tracks based on this logic:

   1. The tracker associates each dynamic grid cell with the existing tracks using a gated nearest-neighbor approach.

   2. The tracker initializes new tracks using unassigned dynamic grid cells. A track is created with a Tentative status, and the status will change to Confirmed after enough updates. For more information, see the Confirmation threshold [M N] parameter.

   3. Alternatively, the tracker confirms a track immediately if the ObjectClassID of the track is a positive value after track initialization. For more information, see the Function to initialize tracks from grid cell sets parameter.

   4. The tracker performs coasting, predicting unassigned tracks to the current time, and deletes tracks with more misses than the specified threshold. For more information, see the Deletion threshold [P Q] parameter.

Track Structure

The fields of the track structure are:

Field	Definition
TrackID	Unique track identifier used to distinguish multiple tracks.
BranchID	Unique track branch identifier used to distinguish multiple track branches.
SourceIndex	Unique source index used to distinguish tracking sources in a multiple tracker environment.
UpdateTime	Time at which the track is updated. Units are in seconds.
Age	Number of times the track survived.
State	Value of the state vector at the update time.
StateCovariance	Uncertainty covariance matrix.
ObjectClassID	Integer value representing the object classification. The value 0 represents an unknown classification. Nonzero classifications apply only to confirmed tracks.
TrackLogic	Confirmation and deletion logic type, returned as 'History' or 'Score'.
TrackLogicState	The current state of the track logic type. Based on the logic type TrackLogic, the logic state is returned as: 'History' – A 1-by-K logical vector, where K is the number of latest track logical states recorded. In the vector, 1 denotes hit and 0 denote miss. 'Score' – A 1-by-2 real-valued vector, [cs ms]. cs is the current score, and ms is the maximum score.
IsConfirmed	Confirmation status. This field is true if the track is confirmed to be a real target.
IsCoasted	Coasting status. This field is true if the track is updated without a new detection.
IsSelfReported	Indicate if the track is reported by the tracker. This field is used in a track fusion environment. It is returned as true by default.
ObjectAttributes	Additional information about the track.

References

[1] Nuss, D., Reuter, S., Thom, M., Yuan, T., Krehl, G., Maile, M., Gern, A. and Dietmayer, K., 2018. A random finite set approach for dynamic occupancy grid maps with real-time application. The International Journal of Robotics Research, 37(8), pp.841-866.

[2] Steyer, Sascha, Georg Tanzmeister, and Dirk Wollherr. "Object tracking based on evidential dynamic occupancy grids in urban environments." In 2017 IEEE Intelligent Vehicles Symposium (IV), pp. 1064-1070. IEEE, 2017.

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using Simulink® Coder™.

Usage notes and limitations:

• The visualization of the grid map does not support code generation.

Version History

Introduced in R2021b

R2023a: Simulink buses do not show in workspace

As of R2023a, the Simulink buses created by this block no longer show in MATLAB workspace.