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Grid-based multi-object tracker

The `trackerGridRFS`

System object™ 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
defined in [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.

To track targets using this object:

Create the

`trackerGridRFS`

object and set its properties.Call the object with arguments, as if it were a function.

To learn more about how System objects work, see What Are System Objects?.

creates a
`tracker`

= trackerGridRFS`trackerGridRFS`

System object with default property values.

sets properties for the tracker using one or more name-value pairs. For example,
`tracker`

= trackerGridRFS(`Name,Value`

)`trackerGridRFS('MaxNumTracks',100)`

creates a grid-based multi-object
tracker that allows a maximum of 100 tracks. Enclose each property name in quotes.

Unless otherwise indicated, properties are *nontunable*, which means you cannot change their
values after calling the object. Objects lock when you call them, and the
`release`

function unlocks them.

If a property is *tunable*, you can change its value at
any time.

For more information on changing property values, see System Design in MATLAB Using System Objects.

`TrackerIndex`

— Unique tracker identifier`0`

(default) | nonnegative integerUnique tracker identifier, specified as a nonnegative integer. This property is used 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 fuser.

**Example: **`1`

`SensorConfigurations`

— Configurations of tracking sensorscell array of

`trackingSensorConfiguration`

objectsConfiguration of tracking sensors, specified as a cell array of `trackingSensorConfiguration`

objects. This property provides the tracking
sensor configuration information, such as sensor detection limits, sensor resolution,
and sensor mounting, to the tracker. There are no default values for the
`SensorConfigurations`

property, and you must specify the
`SensorConfigurations`

property before using the tracker. You can
update the configuration, if the `HasSensorConfigurationsInput`

property is set to `true`

, by specifying the configuration input
argument `configs`

.

When specifying the `trackingSensorConfiguration`

object, the
following properties must be specified with these formats:

Property 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 |

`SensorTransformParameters` | Parameters of sensor transform function, specified as a
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 only one structure is provided, 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 of scalars. 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 tracker ignores the `FilterInitializationFcn`

,
`SensorTransformFcn`

, and `MaxNumDetsPerObject`

properties of the `trackingSensorConfiguration`

object.

`HasSensorConfigurationsInput`

— Enable updating sensor configurations with time`false`

(default) | `true`

Enable updating sensor configurations with time, specified as
`false`

or `true`

. Set this property to
`true`

if you want the configurations of the sensors updated with
time. When this property is set to `true`

, you must specify the
configuration input `configs`

when using this object.

**Data Types: **`logical`

`StateParameters`

— Parameters of track state reference frame`struct([])`

(default) | `struct array`

Parameters of the track state reference frame, specified as a structure or a structure
array. The tracker passes its `StateParameters`

property values to
the `StateParameters`

property of the generated tracks. You can use
these parameters to define the reference frame in which the track is reported or other
desirable attributes of the generated tracks.

For example, you can use the following structure to define a rectangular reference
frame whose origin position is at `[10 10 0]`

meters and whose origin
velocity is [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]` |

**Tunable: **Yes

**Data Types: **`struct`

`MaxNumSensors`

— Maximum number of sensors`20`

(default) | positive integerMaximum number of sensors that can be connected to the tracker, specified as a
positive integer. `MaxNumSensors`

must be greater than or equal to
the largest value of `SensorIndex`

found in all the sensor data and
configurations used to update the tracker.

**Data Types: **`single`

| `double`

`MaxNumTracks`

— Maximum number of tracks`100`

(default) | positive integerMaximum number of tracks that the tracker can maintain, specified as a positive integer.

**Data Types: **`single`

| `double`

`GridLength`

— `100`

(default) | positive scalar*x*-direction dimension of the grid in the local coordinates,
specified as a positive scalar in meters.

`GridWidth`

— `100`

(default) | positive scalar*y*-direction dimension of the grid in the local coordinates,
specified as a positive scalar in meters.

`GridResolution`

— Resolution of grid`1`

(default) | positive scalarResolution of the grid, specified as a positive scalar.
`GridResolution`

represents the number of cells per meter of the
grid for both the *x*- and *y*-direction of the grid.

`GridOriginInLocal`

— Location of grid origin in local coordinate frame`[-50 -50]`

(default) | two-element vector of scalarLocation of the grid origin in the local coordinate frame, specified as a two-element vector of scalars in meters. The grid origin represents the bottom-left corner of the grid.

`MotionModel`

— Motion model for tracking`'constant-velocity'`

(default) | `'constant-acceleration'`

| `'constant-turn-rate'`

Motion model for tracking, specified as `'constant-velocity'`

,
`'constant-acceleration'`

, or
`'constant-turn-rate'`

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

`MotionModel` | 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/s^{2})`ay`

— Acceleration of the object in the y direction of the local tracking frame (m/s^{2})`w`

— Yaw-rate of the object in the local tracking frame (degree/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)

`VelocityLimits`

— Minimum and maximum velocity of objects`[-10 10; -10 10]`

(default) | 2-by-2 matrix of scalarMinimum and maximum velocity of objects, specified 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.

`AccelerationLimits`

— Minimum and maximum acceleration of objects`[-5 5; -5 5]`

(default) | 2-by-2 matrix of scalarMinimum and maximum acceleration of objects, specified as a 2-by-2 matrix of scalars
in m/s^{2}. The first row specifies the lower and upper
acceleration limits in the *x*-direction and the second row specifies
the lower and upper acceleration limits in the *y*-direction . The
tracker uses these limits to sample new particles in the grid using a uniform
distribution.

This property is only active when the `MotionModel`

property is
set to `'constant-acceleration'`

.

`TurnrateLimits`

— Minimum and maximum turn rate of objects`[-5; 5]`

(default) | two-element vector of scalarMinimum and maximum turn rate of objects, specified a two-element vector of scalars in degree/s. The first element defines the minimum turn rate and the second element defines the maximum turn-rate.

This property is only active when the `MotionModel`

property is
set to `'constant-turnrate'`

.

`ProcessNoise`

— Process noise covarianceProcess noise covariance, specified as an
*N*-by-*N* matrix of scalars. This property
specifies the process noise for positions of particles and the geometric centers of
targets.

When the

`HasAdditiveProcessNoise`

property is set to`true`

, the process directly adds to the prediction model. In this case,*N*is equal to the dimension of the particle state.When the

`HasAdditiveProcessNoise`

property is set to`false`

, define the process noise according to the selected motion model. The process noise is added to the higher order terms, such as the acceleration for the`'constant-acceleration'`

model.`MotionModel`

Number of Terms ( *N*)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]`

**Tunable: **Yes

`HasAdditiveProcessNoise`

— Enable to model process noise as additive`false`

(default) | `true`

Enable to model process noise as additive, specified as `true`

or
`false`

. When this property is `true`

, process noise
is added directly to the state vector. Otherwise, noise is incorporated in the motion
model.

**Example: **`true`

`NumParticles`

— Number of particles per grid`10000`

(default) | positive integerNumber of particles per grid, specified as a positive integer. A higher number of particles can improve estimation quality, but can increase computational cost.

`NumBirthParticles`

— Number of newborn particles per time step`1000`

(default) | positive scalarNumber of newborn (initialized) particles per time step, specified 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
`BirthProbability`

property. A reasonable value of the
`NumBirthParticles`

property is approximately ten percent of the
number of particles specified by the `NumParticles`

property.

`BirthProbability`

— Probability of target birth in a cell per step`0.01`

(default) | scalar in `[0 1)`

Probability of target birth in a cell per step, specified 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`

`DeathRate`

— Death rate of particles per unit time`1e-3`

(default) | positive scalarDeath rate of particles per unit time, specified as a positive scalar. Death rate
indicates the possibility that a particle or target vanishes after one time step. Death
rate influences the survival probability (*P*_{s})
of a component between successive time steps as:

$${P}_{\text{s}}={\left(1-{P}_{d}\right)}^{\Delta T}$$

where *Δ**T* is the time
step.

**Example: **`1e-4`

**Tunable: **Yes

`FreeSpaceDiscountFactor`

— Confidence in free space prediction`0.8`

(default) | scalarConfidence in free space prediction, specified as a scalar. In the prediction stage
of the tracker, the belief mass of a cell to be in the "free" (unoccupied) state is
reduced by the `FreeSpaceDiscountFactor`

:

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

where *k* is the time step index,
*m* is the belief mass, *α* is the free space
discount factor, and *Δ**T* is the time step.

**Tunable: **Yes

`Clustering`

— Clustering method used for new object extraction`'DBSCAN'`

(default) | `'Custom'`

Clustering method used for new object extraction, specified 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`ClusteringThreshold`

and`MinNumCellsPerCluster`

properties of the tracker.`'Custom'`

— Cluster unassigned dynamic grid cells using a custom clustering function specified in the`CustomClusteringFcn`

property of the tracker.

`ClusteringThreshold `

— Threshold for DBSCAN clustering`5`

(default) | positive scalarThreshold for DBSCAN clustering, specified as a positive scalar.

To enable this property, set the `Clustering`

property to
`'DBSCAN'`

.

`CustomClusteringFcn`

— Custom function for clustering unassigned grid cellsfunction handle

Custom function for clustering unassigned grid cells, specified as a function handle. The function must support this signature:

function indices = myFunction(dynamicGridCells)

`dynamicGridCells`

is a structure defining 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 array of scalars, where N is
the number of unassigned cells. |

`FreeMass` | Free belief mass of the cells, specified as an
N-element array of scalars, where N is
the number of unassigned cells. |

`indices`

must be returned as an
*N*-element vector of indices defining the cluster index for each
dynamic grid cell.

To enable this property, set the `Clustering`

property to
`'Custom'`

.

`MinNumCellsPerCluster`

— Minimum number of cells per cluster for DBSCAN`2`

(default) | positive integerMinimum number of cells per cluster for DBSCAN, specified as a positive scalar. This property affects whether a point is a core point in the DBSCAN algorithm.

To enable this property, set the `Clustering`

property to
`'DBSCAN'`

.

`TrackInitializationFcn`

— Function to initialize new track`'trackerGridRFS.defaultTrackInitialization'`

(default) | function handleFunction to initialize new tracks, specified as a function handle. The initialization function initiates a track from a set of dynamic grid cells.

The default initialization function merges the Gaussian estimate from each cell to describe the state of the object. The orientation of the object is aligned 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 object calculates uncertainties in length, width, and orientation estimates are calculated using linear approximations.

If you choose to customize your own initialization function, the function must support the following signature:

function track = myFunction(dynamicGridCells)

`dynamicGridCells`

is a structure defining a set of grid cells
initializing the track. It has the following 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 array of scalars, where N is
the number of unassigned cells. |

`FreeMass` | Free belief mass of the cells, specified as an
N-element array of scalars, where N is
the number of unassigned cells. |

`track`

must be returned as an `objectTrack`

object or a structure
whose field names are the same as the property names of an `objectTrack`

object. The dimension
of the state must be the same as the state dimension specified in the
`MotionModel`

property.

`TrackUpdateFcn`

— Function to update existing track`'trackerGridRFS.defaultTrackUpdate'`

(default) | function handleFunction to update an existing track using its associated set of dynamic grid cells, specified as a function handle.

The default update function updates the `State`

and
`StateCovariance`

properties 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 `TrackInitializationFcn`

property.
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)

`predictedTrack`

is the predicted track of an object, specified as an`objectTrack`

object.`dynamicGridCells`

is a structure defining a set of dynamic grid cells associated to 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 array of scalars, where*N*is the number of unassigned cells.`FreeMass`

Free belief mass of the cells, specified as an *N*-element array of scalars, where*N*is the number of unassigned cells.`updatedTrack`

is the updated track, returned as an`objectTrack`

object or a structure whose field names are the same as the property names of an`objectTrack`

object.

`AssignmentThreshold`

— Threshold for assigning dynamic grid cells to tracks`30`

(default) | positive scalarThreshold for assigning dynamic grid cells to tracks, specified 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
`AssignmentThreshold`

value.

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`

`ConfirmationThreshold`

— Threshold for track confirmation`[2 3]`

(default) | 2-element vector of scalarThreshold for track confirmation, specified as a 2-element vector of scalars
`[M N]`

. A track is confirmed if it has been assigned to any dynamic
grid cell in at least `M`

updates of the last `N`

updates.

`DeletionThreshold`

— Threshold for track deletion`[5 5]`

(default) | 2-element vector of scalarThreshold for track deletion, specified as a 2-element vector of scalars ```
[P
R]
```

. A track is deleted if has not been assigned to any dynamic grid cell in
at least `P`

updates of the last `R`

updates.

**Example: **`0.01`

**Data Types: **`single`

| `double`

`NumTracks`

— Number of tracks maintained by trackernonnegative integer

This property is read-only.

Number of tracks maintained by the tracker, returned as a nonnegative integer.

**Data Types: **`double`

`NumConfirmedTracks`

— Number of confirmed tracksnonnegative integer

This property is read-only.

Number of confirmed tracks, returned as a nonnegative integer. If the
`IsConfirmed`

property of an output track object is
`true`

, the track is confirmed.

**Data Types: **`double`

`UseGPU`

— Enable using GPU for estimation of dynamic grid map`false`

(default) | `true`

This property is read-only.

Enable using GPU for estimation of the dynamic grid map, specified as
`true`

or `false`

. Enabling GPU computation requires
the Parallel Computing Toolbox™.

returns a list of confirmed tracks that are updated from a list of sensor data
`confirmedTracks`

= tracker(`sensorData`

,`time`

)`sensorData`

at the update time `time`

. Confirmed
tracks are corrected and predicted to the update time.

also specifies the configurations of sensors `confirmedTracks`

= tracker(`sensorData`

,`configs`

,`time`

)`configs`

. To enable this
syntax, set the `HasSensorConfigurationsInput`

property to
`true`

.

`[`

also returns a list of tentative tracks `confirmedTracks`

,`tentativeTracks`

,`allTracks`

] = tracker(___)`tentativeTracks`

and a list of
all tracks `allTracks`

. You can use any combination of input arguments
from previous syntaxes.

`[`

additionally returns the evidential grid map maintained in the tracker. You can use the
returned map to obtain details on the estimates.`confirmedTracks`

,`tentativeTracks`

,`allTracks`

,`map`

] = tracker(___)

`sensorData`

— Sensor dataSensor data, specified as an *N*-element array of structures.
Each structure must define the measurement from a high resolution sensor using the
these fields:

Fields | Description |
---|---|

`Time` | Time at which the sensor reports the data, specified as a nonnegative scalar. |

`SensorIndex` | Unique identifier of the sensor, specified as a positive integer. |

`Measurement` | Measurements of the sensor, specified a
K-by-M matrix of scalars.
K is the dimension of measurements, 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 measurement. See Object Detections for more details. |

The `Time`

value must be less than or equal to the current update
time, `time`

, and greater than the previous time value used to
update the tracker.

`time`

— Time of updatescalar

Time of update, specified as a scalar. The tracker updates all tracks to this time. Units are in seconds.

`time`

must be greater than or equal to the largest
`Time`

field value of the `sensorData`

structures. `time`

must increase in value with each update to the
tracker.

**Data Types: **`single`

| `double`

`configs`

— Sensor configurationsarray of structures | cell array of structures | cell array of

`trackingSensorConfiguration`

objectsSensor configurations, specified as an array of structures, a cell array of
structures, or a cell array of `trackingSensorConfiguration`

objects. If you specify the value using an
array of structures or a cell array of structures, you must include
`SensorIndex`

as a field in each structure. The other optional
fields in each structure must have the same names as the
`trackingSensorConfiguration`

object properties. You only need to
specify sensor configurations that need to be updated. For example, if you want to
update the `IsValidTime`

property for only the fifth sensor,
specify `configs`

as
`struct('SensorIndex',5,'IsValidTime',false)`

.

To enable this argument, set the
`HasSensorConfigurationsInput`

property to
`true`

.

`confirmedTracks`

— Confirmed tracksarray of

`objectTrack`

objectsConfirmed tracks updated to the current time, returned as an array of `objectTrack`

objects, where each
element represents the track of an object. The state form of each track follows the
form specified in the `MotionModel`

property.

`tentativeTracks`

— Tentative tracksarray of

`objectTrack`

objectsTentative tracks, returned as an array of `objectTrack`

objects, where each
element represents the track of an object. The state form of each track follows the
form specified in the `MotionModel`

property.

`allTracks`

— All tracksstructure | array of objects

All tracks, returned as an array of `objectTrack`

objects, where each
element represents the track of an object. The state form of each track follows the
form specified in the `MotionModel`

property.

`map`

— Dynamic evidential grid map`dynamicEvidentialGridMap`

objectDynamic evidential grid map, returned as a `dynamicEvidentialGridMap`

object.

To use an object function, specify the
System object as the first input argument. For
example, to release system resources of a System object named `obj`

, use
this syntax:

release(obj)

`trackerGridRFS`

`predictTracksToTime` | Predict track state |

`predictMapToTime` | Predict dynamic map to a time stamp |

`showDynamicMap` | Plot dynamic occupancy grid map |

Create a tracking scenario.

scene = trackingScenario('UpdateRate',5,'StopTime',5); rng(2021); % For reproducible results

Add a platform with a mounted lidar sensor to the tracking scenario.

plat = platform(scene); lidar = monostaticLidarSensor(1,'DetectionCoordinates','Body','HasOrganizedOutput',false);

Add two targets with random positions and velocities to the scenario. Also, define the trajectory, mesh, and dimension of each platform.

for i = 1:2 target = platform(scene); x = 50*(2*rand - 1); y = 50*(2*rand - 1); vx = 5*(2*rand - 1); vy = 5*(2*rand - 1); target.Trajectory.Position = [x y 0]; target.Trajectory.Velocity = [vx vy 0]; % Align the orientation of the target with the direction of motion. target.Trajectory.Orientation = quaternion([atan2d(vy,vx),0,0],'eulerd','ZYX','frame'); target.Mesh = extendedObjectMesh('sphere'); target.Dimensions = struct('Length',4,'Width',4,'Height',2,'OriginOffset',[0 0 0]); end

Define the configuration of the lidar sensor.

config = trackingSensorConfiguration(1,... 'SensorLimits',[-180 180;0 100],... 'SensorTransformParameters',struct,... 'IsValidTime',true);

Create a grid-based tracker.

tracker = trackerGridRFS('SensorConfigurations',config,... 'AssignmentThreshold',5,... 'MinNumCellsPerCluster',4,... 'ClusteringThreshold',3);

Define a `theaterPlot`

object and two associated plotters for visualizing the tracking scene.

tp = theaterPlot('XLimits',[-50 50],'YLimits',[-50 50]); trkPlotter = trackPlotter(tp,'DisplayName','Tracks','MarkerFaceColor','g'); tthPlotter = platformPlotter(tp,'DisplayName','Truths','MarkerFaceColor','r','ExtentAlpha',0.2);

Advance the scenario and run the tracker with the lidar data.

while advance(scene) time = scene.SimulationTime; % Generate point cloud. tgtMeshes = targetMeshes(plat); [ptCloud, config] = lidar(tgtMeshes, time); % Format the data for the tracker. sensorData = struct('Time',time,... 'SensorIndex',1,... 'Measurement',ptCloud',... 'MeasurementParameters',struct... ); % Update the tracker using the sensor data. tracks = tracker(sensorData, time); % Visualize tracks. pos = zeros(numel(tracks),3); vel = zeros(numel(tracks),3); orient = quaternion.ones(numel(tracks),1); dim = repmat(plat.Dimensions,numel(tracks),1); ids = string([tracks.TrackID]); for i = 1:numel(tracks) vel(i,:) = [tracks(i).State(2);tracks(i).State(4);0]; pos(i,:) = [tracks(i).State(1);tracks(i).State(3);0]; dim(i).Length = tracks(i).State(6); dim(i).Width = tracks(i).State(7); orient(i) = quaternion([tracks(i).State(5) 0 0],'eulerd','ZYX','frame'); end trkPlotter.plotTrack(pos,dim,orient,ids); % Visualize platforms. pos = vertcat(tgtMeshes.Position); meshes = vertcat(tgtMeshes.Mesh); orient = vertcat(tgtMeshes.Orientation); tthPlotter.plotPlatform(pos,meshes,orient); end

The `trackerGridRFS`

system object initializes, confirms, and deletes tracks
automatically by using this algorithm:

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.

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

The tracker manage tracks based on this logic:

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

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`ConfirmationThreshold`

property.Alternatively, the tracker confirms a track immediately if the

`ObjectClassID`

of the track is set to a positive value after track initialization. For more information, see the`TrackInitializationFcn`

property.The tracker performs coasting, predicting unassigned tracks to the current time, and deletes tracks with more misses than allowed. For more information, see the

`DeletionThreshold`

property.

[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.

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