trackerTOMHT
Multi-hypothesis, multi-sensor, multi-object tracker
Description
The trackerTOMHT
System object™ is a multi-hypothesis tracker capable of processing detections of multiple
targets from multiple sensors. The tracker initializes, confirms, predicts, corrects, and
deletes tracks. Inputs to the tracker are detection reports generated by objectDetection
, fusionRadarSensor
,
irSensor
, or
sonarSensor
objects. The tracker estimates the state vector and state vector covariance matrix for each
track. The tracker assigns detections based on a track-oriented, multi-hypothesis approach.
Each detection is assigned to at least one track. If the detection cannot be assigned to any
track, the tracker creates a track.
Any new track starts in a tentative state. If enough detections are
assigned to a tentative track, its status changes to confirmed. If the
detection already has a known classification (the ObjectClassID
field of
the returned track is nonzero), that track is confirmed immediately. When a track is
confirmed, the multi-object tracker considers the track to represent a physical object. If
detections are not assigned to the track within a specifiable number of updates, the track is
deleted. For an overview of how the tracker functions, see Algorithms.
To track objects using the multi-hypothesis tracker:
Create the
trackerTOMHT
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?
Creation
Description
creates a
tracker
= trackerTOMHTtrackerTOMHT
System object with default property values.
sets properties for the multi-object tracker using one or more name-value pairs. For
example,
tracker
= trackerTOMHT(Name,Value
)trackerTOMHT('FilterInitializationFcn',@initcvukf,'MaxNumTracks',100)
creates a multi-object tracker that uses a constant-velocity, unscented Kalman filter and
allows a maximum of 100 tracks. Enclose each property name in quotes.
Properties
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 integer
Unique 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
FilterInitializationFcn
— Filter initialization function
@initcvekf
(default) | function handle | character vector
Filter initialization function, specified as a function handle or as a character vector containing the name of a filter initialization function. The tracker uses a filter initialization function when creating new tracks.
Sensor Fusion and Tracking Toolbox™ supplies many initialization functions that you can use to specify
FilterInitializationFcn
.
Initialization Function | Function Definition |
---|---|
initcvabf | Initialize constant-velocity alpha-beta filter |
initcaabf | Initialize constant-acceleration alpha-beta filter |
initcvekf | Initialize constant-velocity extended Kalman filter. |
initcackf | Initialize constant-acceleration cubature filter. |
initctckf | Initialize constant-turn-rate cubature filter. |
initcvckf | Initialize constant-velocity cubature filter. |
initcapf | Initialize constant-acceleration particle filter. |
initctpf | Initialize constant-turn-rate particle filter. |
initcvpf | Initialize constant-velocity particle filter. |
initcvkf | Initialize constant-velocity linear Kalman filter. |
initcvukf | Initialize constant-velocity unscented Kalman filter. |
initcaekf | Initialize constant-acceleration extended Kalman filter. |
initcakf | Initialize constant-acceleration linear Kalman filter. |
initcaukf | Initialize constant-acceleration unscented Kalman filter. |
initctekf | Initialize constant-turn-rate extended Kalman filter. |
initctukf | Initialize constant-turn-rate unscented Kalman filter. |
initctrvekf | Initialize constant-turn-rate and velocity magnitude extended Kalman filter. |
initctrvukf | Initialize constant-turn-rate and velocity magnitude unscented Kalman filter. |
initcvmscekf | Initialize constant-velocity modified spherical coordinates extended Kalman filter. |
initrpekf | Initialize constant-velocity range-parametrized extended Kalman filter. |
initapekf | Initialize constant-velocity angle-parametrized extended Kalman filter. |
initekfimm | Initialize tracking IMM filter. |
initsingerekf | Initialize singer acceleration extended Kalman filter. |
initvisionbboxkf | Initialize constant-velocity linear Kalman filter for 2-D axis-aligned bounding box. |
You can also write your own initialization function. The function must have the following syntax:
filter = filterInitializationFcn(detection)
objectDetection
. The output of this function must be a filter object:
trackingKF
, trackingEKF
, trackingUKF
, trackingCKF
, trackingPF
,
trackingMSCEKF
, trackingGSF
, trackingIMM
, or trackingABF
.
To guide you in writing this function, you can examine the details of the supplied functions from within MATLAB®. For example:
type initcvekf
Data Types: function_handle
| char
MaxNumTracks
— Maximum number of tracks
100
(default) | positive integer
Maximum number of tracks that the tracker can maintain, specified as a positive integer.
Data Types: single
| double
MaxNumSensors
— Maximum number of sensors
20
(default) | positive integer
Maximum 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 detections used to update the tracker. SensorIndex
is
a property of an objectDetection
object.
The MaxNumSensors
property determines how many sets of
ObjectAttributes
fields each output track can
have.
Data Types: single
| double
MaxNumDetections
— Maximum number of detections
Inf
(default) | positive integer
Maximum number of detections that the tracker can take as inputs, specified as a positive integer.
Data Types: single
| double
OOSMHandling
— Handle out-of-sequence measurement (OOSM)
'Terminate'
(default) | 'Neglect'
Handle out-of-sequence measurement (OOSM), specified as
'Terminate'
or 'Neglect'
. Each detection has a
timestamp associated with it, td, and the
tracker has it own timestamp, tt, which is
updated in each call. The tracker considers a measurement as an OOSM if
td <
tt.
When the property is specified as
'Terminate'
— The tracker stops running when it encounters any out-of-sequence measurements.'Neglect'
— The tracker neglects any out-of-sequence measurements and continue to run.
To simulate out-of-sequence detections, use objectDetectionDelay
.
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
MaxNumHypotheses
— Maximum number of hypotheses to maintain
5
(default) | positive integer
Maximum number of global assignment hypotheses maintained by the tracker at each step, specified as a positive integer. If the number of hypotheses is larger than the property value, the tracker prunes the hypotheses of lower likelihoods and its associated branches until the number of hypotheses reaches the property value. Larger values increase the computational load.
Example: 10
Data Types: single
| double
MaxNumTrackBranches
— Maximum number of track branches per track
3
(default) | positive scalar
Maximum number of track branches allowed for each track, specified as a positive integer. Larger values increase the computational load.
Data Types: single
| double
MaxNumHistoryScans
— Maximum number of scans maintained in the branch history
4
(default) | positive integer
Maximum number of scans maintained in the branch history, specified as a positive integer. The number of track history scans is typically from 2 through 6. Larger values increase the computational load.
Example: 6
Data Types: single
| double
AssignmentThreshold
— Detection assignment threshold
30*[0.3 0.7 1 Inf]
(default) | positive scalar | 1-by-3 vector of positive values | 1-by-4 vector of positive values
Detection assignment threshold, specified as a positive scalar, a 1-by-3 vector of
non-decreasing positive values,
[C1,C2,C3],
or an1-by-4 vector of non-decreasing positive values,
[C1,C2,C3,C4].
If specified as a scalar, the specified value, val, will be expanded
to [0.3,0.7,1,Inf
]*val. If specified as
[C1,C2,C3],
it will be expanded as
[C1,C2,C3,Inf
].
The thresholds control (1) the assignment of a detection to a track, (2) the creation of a new branch from a detection, and (3) the creation of a new branch from an unassigned track. The threshold values must satisfy: C1 <= C2 <= C3<=C4.
C1 defines a distance such that if a track has an assigned detection with lower distance than C1, the track is no longer considered unassigned and does not create an unassigned track branch.
C2 defines a distance that if a detection has been assigned to a track with lower distance than C2, the detection is no longer considered unassigned and does not create a new track branch.
C3 defines the maximum distance for assigning a detection to a track.
C4 defines combinations of track and detection for which an accurate normalized cost calculation is performed. Initially, the tracker executes a coarse estimation for the normalized distance between all the tracks and detections. The tracker only calculates the accurate normalized distance for the combinations whose coarse normalized distance is less than C4.
See Algorithms for an explanation of the normalized distance.
Increase the value of C3 if there are detections that should be assigned to tracks but are not. Decrease the value if there are detections that are assigned to tracks they should not be assigned to (too far away).
Increasing the values C1 and C2 helps control the number of track branches that are created. However, doing so reduces the number of branches (hypotheses) each track has.
Increase the value of C4 if there are combinations of track and detection that should be calculated for assignment but are not. Decrease it if cost calculation takes too much time.
Note
If the value of C4 is finite, the state transition function and measurement function, specified in the tracking filter used in the tracker, must be able to take an M-by-N matrix of states as input and output N predicted states and N measurements, respectively. M is the size of the state. N, the number of states, is an arbitrary nonnegative integer.
Tunable: Yes
Data Types: single
| double
ConfirmationThreshold
— Minimum score required to confirm track
20
(default) | positive scalar
Minimum score required to confirm a track, specified as a positive scalar. Any track with a score higher than this threshold is confirmed.
Example: 12
Tunable: Yes
Data Types: single
| double
DeletionThreshold
— Maximum score drop for track deletion
-7
(default) | scalar
The maximum score drop before a track is deleted, specified as a negative scalar. Any track with a score that falls by more than this parameter from the maximum score is deleted. Deletion threshold is affected by the probability of false alarm.
Example: 12
Tunable: Yes
Data Types: single
| double
DetectionProbability
— Probability of detection used for track score
0.9
(default) | positive scalar between 0 and 1
Probability of detection, specified as a positive scalar between 0 and 1. This property is used to compute track score.
Example: 0.5
Tunable: Yes
Data Types: single
| double
FalseAlarmRate
— Probability of false alarm used for track score
1e-6
(default) | scalar
The probability of false alarm, specified as a scalar. This property is used to compute track score.
Example: 1e-5
Tunable: Yes
Data Types: single
| double
Beta
— Rate of new tracks per unit volume
1
(default) | positive scalar
The rate of new tracks per unit volume, specified as a positive scalar. The rate of new tracks is used in calculating the track score during track initialization.
Example: 2.5
Tunable: Yes
Data Types: single
| double
Volume
— Volume of sensor measurement bin
1
(default) | positive scalar
The volume of a sensor measurement bin, specified as a positive scalar. For example, if a radar produces a 4-D measurement, which includes azimuth, elevation, range, and range rate, the 4-D volume is defined by the radar angular beam width, the range bin width and the range-rate bin width. Volume is used in calculating the track score when initializing and updating a track.
Example: 1.5
Tunable: Yes
Data Types: single
| double
MinBranchProbability
— Minimum probability required to keep track
.001
(default) | positive scalar
Minimum probability required to keep a track, specified as a positive scalar less than one. Any track with lower probability is pruned. Typical values are 0.001 to 0.005.
Example: .003
Tunable: Yes
Data Types: single
| double
NScanPruning
— N-scan pruning method
'None'
(default) | 'Hypothesis'
N-scan pruning method, specified as 'None'
or
'Hypothesis'
. In N-scan pruning, branches that belong to the same
track are pruned (deleted) if, in the N-scans history, they contradict the most likely
branch for the same track. The most-likely branch is defined in one of two ways:
'None'
– No N-scan pruning is performed.'Hypothesis'
– The chosen branch is in the most likely hypothesis.
Example: 'Hypothesis'
HasCostMatrixInput
— Enable cost matrix input
false
(default) | true
Enable a cost matrix, specified as false
or
true
. If true
, you can provide an assignment cost
matrix as an input argument when calling the object.
Data Types: logical
HasDetectableBranchIDsInput
— Enable input of detectable branch IDs
false
(default) | true
Enable the input of detectable branch IDs at each object update, specified as
false
or true
. Set this property to
true
if you want to provide a list of detectable branch IDs. This
list tells the tracker of all branches that the sensors are expected to detect and,
optionally, the probability of detection for each branch.
Data Types: logical
OutputRepresentation
— Track output method
'Tracks'
(default) | 'Hypothesis'
| 'Clusters'
Track output method, specified as 'Tracks'
, 'Hypothesis'
, or 'Clusters'
.
'Tracks'
– Output the centroid of each track based on its track branches.'Hypothesis'
– Output branches that are in certain hypotheses. If you choose this option, list the hypotheses to output using theHypothesesToOutput
property.'Clusters'
– Output the centroid of each cluster. Similar to'Tracks'
output, but includes all tracks within a cluster.
Data Types: char
HypothesesToOutput
— Indices of hypotheses to output
1
(default) | positive integer | array of positive integers
Indices of hypotheses to output, specified as an array of positive integers. The indices must all be less than or equal to the maximum number of hypotheses provided by the tracker.
Tunable: Yes
Data Types: single
| double
NumTracks
— Number of tracks maintained by tracker
nonnegative 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 tracks
nonnegative integer
This property is read-only.
Number of confirmed tracks, returned as a nonnegative integer. If the
IsConfirmed
field of an output track structure is
true
, the track is confirmed.
Data Types: double
Usage
To process detections and update tracks, call the tracker with arguments, as if it were a function (described here).
Syntax
Description
returns a list of confirmed tracks that are updated from a list of detections,
confirmedTracks
= tracker(detections
,time
)detections
, at the update time, time
.
Confirmed tracks are corrected and predicted to the update time.
also specifies a cost matrix, confirmedTracks
= tracker(detections
,time
,costMatrix
)costMatrix
.
To enable this syntax, set the HasCostMatrixInput
property to
true
.
also specifies a list of expected detectable branches,
confirmedTracks
= tracker(___,detectableBranchIDs
)detectableBranchIDs
.
To enable this syntax, set the HasDetectableBranchIDsInput
property to true
.
[
also returns a list of tentative tracks, confirmedTracks
,tentativeTracks
,allTracks
] = tracker(___)tentativeTracks
, and a list
of all tracks, allTracks
.
w
also returns information,
analysisInformation
, useful for track analysis.
Input Arguments
detections
— Detection list
cell array of objectDetection
objects
Detection list, specified as a cell array of objectDetection
objects. The Time
property value of
each objectDetection
object must be less than or equal
to the current update time, time
, and greater than the previous
time value used to update the tracker. Also, the Time
differences
between different objectDetection
objects in the cell
array do not need to be equal.
time
— Time of update
scalar
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
property value of the objectDetection
objects in the input detections
list.
time
must increase in value with each update to the
tracker.
Data Types: single
| double
costMatrix
— Cost matrix
real-valued N-by-M matrix
Cost matrix, specified as a real-valued N-by-M
matrix, where N is the number of branches, and M
is the number of current detections. The cost matrix rows must be in the same order as
the list of branches. The columns must be in the same order as the list of detections.
Obtain the correct order of the list of branches using the
getBranches
object function. Matrix columns correspond to the
detections.
At the first update of the object or when the tracker has no previous tracks, specify
the cost matrix to have a size of [0,numDetections]
. Note that the
cost must be calculated so that lower costs indicate a higher likelihood of assigning a
detection to a track. To prevent certain detections from being assigned to certain
tracks, set the appropriate cost matrix entry to Inf
.
Dependencies
To enable this argument, set the HasCostMatrixInput
property
to true
.
Data Types: double
| single
detectableBranchIDs
— Detectable branch IDs
real-valued M-by-1 vector | real-valued M-by-2 matrix
Detectable branch IDs, specified as a real-valued M-by-1
vector or M-by-2 matrix. Detectable branches are branches that the
sensors expect to detect. The first column of the matrix contains a list of branch IDs
of tracks reported in the branchID
field of the track output
arguments. The second column contains the detection probability for the branch.
Sensors can report detection probability, but if not reported, detection probabilities
are obtained from the DetectionProbability
property.
Branches whose identifiers are not included in
detectableBranchIDs
are considered as undetectable. The track
deletion logic does not count the lack of detection as a 'miss' for branch deletion
purposes.
Dependencies
To enable this input argument, set the
HasDetectableBranchIDs
property to
true
.
Data Types: single
| double
Output Arguments
confirmedTracks
— Confirmed tracks
array of objectTrack
objects | array of structures
Confirmed tracks, returned as an array of objectTrack
objects in MATLAB or as an array of structures in code generation. In code
generation, the field names of the returned structure are identical to the
property names of objectTrack
.
The tracker confirms a track if it satisfies the confirmation threshold specified in the
ConfirmationThreshold
property. In that case,
the IsConfirmed
property of the object or field of the
structure is true
.
Data Types: struct
| object
tentativeTracks
— Tentative tracks
array of objectTrack
objects | array of structures
Tentative tracks, returned as an array of objectTrack
objects in
MATLAB or as an array of structures in code generation. In code generation, the
field names of the returned structure are identical to the property names of
objectTrack
.
A track is tentative if it does not satisfy the confirmation threshold specified in the
ConfirmationThreshold
property. In that case, the
IsConfirmed
property of the object or field of the structure is
false
.
Data Types: struct
| object
allTracks
— All tracks
array of objectTrack
objects | array of structures
All tracks, returned as an array of objectTrack
objects in
MATLAB or as an array of structures in code generation. In code generation, the
field names of the returned structure are identical to the property names of
objectTrack
. allTracks
consists of confirmed
and tentative tracks.
Data Types: struct
| object
analysisInformation
— Additional information for analyzing track updates
structure
Additional information for analyzing track updates, returned as a structure. The fields of this structure are:
Field | Description |
OOSMDetectionIndices | Indices of out-of-sequence measurements |
BranchIDsAtStepBeginning | Branch IDs when update began. |
CostMatrix | Cost of kinematic assignment matrix, in which the (i, j) element denotes the cost of assigning track i to detection j. |
Assignments | Assignments returned from |
UnassignedTracks | IDs of unassigned branches returned from the tracker |
UnassignedDetections | Indices of unassigned detections in the
|
InitialBranchHistory | Branch history after branching and before pruning. |
InitialBranchScores | Branch scores before pruning. |
KeptBranchHistory | Branch history after initial pruning. |
KeptBranchScores | Branch scores after initial pruning. |
Clusters | Logical array mapping branches to clusters. Branches belong in the same cluster if they share detections in their history or belong to the same track, either directly or through other branches. Such branches are incompatible. |
TrackIncompatibility | Branch incompatibility matrix. The
|
GlobalHypotheses | Logical matrix mapping branches to global hypotheses. Compatible branches can belong in the same hypotheses. |
GlobalHypScores | Total score of global hypotheses. |
PrunedBranches | Logical array of branches that the |
GlobalBranchProbabilities | Global probability of each branch existing in the global hypotheses. |
BranchesDeletedByPruning | Branches deleted by the tracker. |
BranchIDsAtStepEnd | Branch IDs when the update ended. |
Data Types: struct
Object Functions
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)
Specific to trackerTOMHT
getTrackFilterProperties | Obtain track filter properties |
setTrackFilterProperties | Set track filter properties |
getBranches | Lists track branches |
predictTracksToTime | Predict track state |
initializeTrack | Initialize new track |
deleteTrack | Delete existing track |
initializeBranch | Initialize new track branch |
confirmBranch | Confirm track branch |
deleteBranch | Delete existing track branch |
generateCode | Generate code for tracker object and object functions |
exportToSimulink | Export tracker or track fuser to Simulink model |
Examples
Track Two Objects Using trackerTOMHT
Create the trackerTOMHT
System object with a constant-velocity Kalman filter initialization function, initcvkf
.
tracker = trackerTOMHT('FilterInitializationFcn',@initcvkf, ... 'ConfirmationThreshold',20, ... 'DeletionThreshold',-7, ... 'MaxNumHypotheses',10);
Update the tracker with two detections having nonzero ObjectClassID
. The detections immediately create confirmed tracks.
detections = {objectDetection(1,[10;0],'SensorIndex',1, ... 'ObjectClassID',5,'ObjectAttributes',{struct('ID',1)}); ... objectDetection(1,[0;10],'SensorIndex',1, ... 'ObjectClassID',2,'ObjectAttributes',{struct('ID',2)})}; time = 2; tracks = tracker(detections,time);
Find and display the positions and velocities.
positionSelector = [1 0 0 0; 0 0 1 0]; velocitySelector = [0 1 0 0; 0 0 0 1]; positions = getTrackPositions(tracks,positionSelector)
positions = 2×2
10.0000 0
0 10.0000
velocities = getTrackVelocities(tracks,velocitySelector)
velocities = 2×2
0 0
0 0
Algorithms
Tracker Logic Flow
When you process detections using the tracker, track creation and management follow these steps.
The tracker attempts to assign detections to existing tracks.
The track allows for multiple hypotheses about the assignment of detections to tracks.
Unassigned detections result in the creation of new tracks.
Assignments of detections to tracks create branches for the assigned tracks.
Tracks with no assigned detections are coasted (predicted).
All track branches are scored. Branches with low initial scores are pruned.
Clusters of branches that share detections (incompatible branches) in their history are generated.
Global hypotheses of compatible branches are formulated and scored.
Branches are scored based on their existence in the global hypotheses. Low-scored branches are pruned.
Additional pruning is performed based on N-scan history.
All tracks are corrected and predicted to the input time.
Assignment Thresholds for Multi-Hypothesis Tracker
Three assignment thresholds, C1 , C2, and C3, control (1) the assignment of a detection to a track, (2) the creation of a new branch from a detection, and (3) the creation of a new branch from an unassigned track. The threshold values must satisfy: C1 <= C2 <= C3.
If the cost of an assignment is C = costmatrix(i,j)
, the following
hypotheses are created based on comparing the cost to the values of the assignment
thresholds. Below each comparison, there is a list of the possible hypotheses.
Tips:
Increase the value of C3 if there are detections that should be assigned to tracks but are not. Decrease the value if there are detections that are assigned to tracks they should not be assigned to (too far away).
Increasing the values C1 and C2 helps control the number of track branches that are created. However, doing so reduces the number of branches (hypotheses) each track has.
To allow each track to be unassigned, set C1 = 0.
To allow each detection to be unassigned, set C2 = 0.
Data Precision
All numeric inputs can be single or double precision, but they all must have the same precision.
References
[1] Werthmann, J. R.. "Step-by-Step Description of a Computationally Efficient Version of Multiple Hypothesis Tracking." In International Society for Optics and Photonics, Vol. 1698, pp. 228-301, 1992.
[2] Blackman, S., and R. Popoli. Design and Analysis of Modern Tracking Systems. Artech House Radar Library, Boston, 1999.
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
Usage notes and limitations:
See System Objects in MATLAB Code Generation (MATLAB Coder).
All the detections used with a multi-object tracker must have properties with the same sizes and types.
If you use the
ObjectAttributes
field within anobjectDetection
object, you must specify this field as a cell containing a structure. The structure for all detections must have the same fields and the values in these fields must always have the same size and type. The form of the structure cannot change during simulation.If
ObjectAttributes
are contained in the detection, theSensorIndex
value of the detection cannot be greater than 10.The first update to the multi-object tracker must contain at least one detection.
Version History
Introduced in R2018bR2024a: Tune additional properties
You can tune the following properties:
AssignmentThreshold
ConfirmationThreshold
DeletionThreshold
DetectionProbability
FalseAlarmRate
Volume
Beta
MinBranchProbability
R2024a: Generate code automatically
You can generate C/C++ code automatically by using the generateCode
object function.
See Also
Functions
Objects
objectDetection
|trackingKF
|trackingEKF
|trackingKF
|trackingUKF
|trackingCKF
|trackingPF
|trackingMSCEKF
|trackingGSF
|trackingIMM
|trackingABF
|objectTrack
|fusionRadarSensor
|sonarSensor
|irSensor
|trackerGNN
Blocks
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