trackerJPDA
Joint probabilistic data association tracker
Description
ThetrackerJPDA
System object™ is a tracker capable of processing detections of multiple targets from multiple sensors. The tracker uses joint probabilistic data association to assign detections to each track. The tracker applies a soft assignment where multiple detections can contribute to each track. The tracker initializes, confirms, corrects, predicts (performs coasting), and deletes tracks. Inputs to the tracker are detection reports generated byobjectDetection
,fusionRadarSensor
,irSensor
, orsonarSensor
objects. The tracker estimates the state vector and state estimate error covariance matrix for each track. Each detection is assigned to at least one track. If the detection cannot be assigned to any existing track, the tracker creates a new track.
Any new track starts in atentativestate. If enough detections are assigned to a tentative track, its status changes toconfirmed(see theConfirmationThreshold
property). If the detection already has a known classification (i.e., theObjectClassID
field of the returned track is nonzero), that corresponding track is confirmed immediately. When a track is confirmed, the 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.
You can enable different JPDA tracking modes by specifying theTrackLogicandMaxNumEventsproperties.
Setting theTrackLogicproperty to
'Integrated'
to enable the joint integrated data association (JIPDA) tracker, in which track confirmation and deletion is based on the probability of track existence.Setting theMaxNumEventsproperty to a finite integer to enable the k-best joint integrated data association (k-best JPDA) tracker, which generates a maximum of k events per cluster.
To track targets using this object:
Create the
trackerJPDA
object and set its properties.Call the object with arguments, as if it were a function.
To learn more about how System objects work, seeWhat Are System Objects?
Creation
Description
creates atracker
= trackerJPDAtrackerJPDA
System object with default property values.
sets properties for the tracker using one or more name-value pairs. For example,tracker
= trackerJPDA(Name,Value
)trackerJPDA('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 arenontunable, which means you cannot change their values after calling the object. Objects lock when you call them, and therelease
function unlocks them.
If a property istunable, you can change its value at any time.
For more information on changing property values, seeSystem Design in MATLAB Using System Objects.
TrackerIndex
—Unique tracker identifier
0
(default) |非负整数
Unique tracker identifier, specified as a nonnegative integer. This property is used as theSourceIndex
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 valid 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 specifyFilterInitializationFcn
for atrackerJPDA
object.
Initialization Function | Function Definition |
---|---|
initcvkf |
Initialize constant-velocity linear Kalman filter. |
initcakf |
Initialize constant-acceleration linear Kalman filter. |
initcvabf |
Initialize constant-velocity alpha-beta filter |
initcaabf |
Initialize constant-acceleration alpha-beta filter |
initcvekf |
Initialize constant-velocity extended Kalman filter. |
initcaekf |
Initialize constant-acceleration extended Kalman filter. |
initrpekf |
Initialize constant-velocity range-parametrized extended Kalman filter. |
initapekf |
Initialize constant-velocity angle-parametrized extended Kalman filter. |
initctekf |
Initialize constant-turn-rate extended Kalman filter. |
initcackf |
Initialize constant-acceleration cubature filter. |
initctckf |
Initialize constant-turn-rate cubature filter. |
initcvckf |
Initialize constant-velocity cubature filter. |
initcvukf |
Initialize constant-velocity unscented Kalman filter. |
initcaukf |
Initialize constant-acceleration unscented Kalman filter. |
initctukf |
Initialize constant-turn-rate unscented Kalman filter. |
initcvmscekf |
Initialize constant-velocity extended Kalman filter in modified spherical coordinates. |
initekfimm |
Initialize tracking IMM filter. |
You can also write your own initialization function using the following syntax:
filter = filterInitializationFcn(detection)
objectDetection
. The output of this function must be a filter object:trackingKF
,trackingEKF
,trackingUKF
,trackingCKF
,trackingGSF
,trackingIMM
,trackingMSCEKF
, ortrackingABF
.
For guidance in writing this function, use thetype
command to examine the details of built-in MATLAB®functions. For example:
type
initcvekf
Note
trackerJPDA
does not accept all filter initialization functions in Sensor Fusion and Tracking Toolbox. The full list of filter initialization functions available in Sensor Fusion and Tracking Toolbox are given in theInitializationsection ofEstimation Filters.
Data Types:function_handle
|char
MaxNumEvents
—Value of k for k-best JPDA
Inf
(default) |positive integer
Value of k for k-best JPDA, specified as a positive integer. This property defines the maximum number of feasible joint events for the track and detection association of each cluster. Setting this property to a finite value enables you to run a k-best JPDA tracker, which generates a maximum of k events per cluster.
Data Types:single
|double
EventGenerationFcn
—Feasible joint events generation function
@jpdaEvents
(default) |function handle|character vector
Feasible joint events generation function, specified as a function handle or as a character vector containing the name of a feasible joint events generation function. A generation function generates feasible joint event matrices from admissible events (usually given by a validation matrix or a likelihood matrix) of a scenario. For details, seejpadEvents
.
You can also write your own generation function.
If the
MaxNumEvents
property is set toInf
, the function must have the following syntax:FJE = myfunction (ValidationMatrix)
jpdaEvents
.If the
MaxNumEvents
property is set to a finite value, the function must have the following syntax:(FJE,FJEProbs] = myfunction(likelihoodMatrix,k)
jpdaEvents
.
For guidance in writing this function, use thetype
command to examine the details ofjpdaEvents
:
typejpdaEvents
Example:@myfunction
or'myfunction'
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 ofSensorIndex
found in all the detections used to update the tracker.SensorIndex
is a property of anobjectDetection
object. TheMaxNumSensors
property determines how many sets ofObjectAttributes
each 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'
|'Retrodiction'
Handling of out-of-sequence measurement (OOSM), specified as'Terminate'
,'Neglect'
, or'Retrodiction'
. Each detection has an associated timestamp,td, and the tracker has its own timestamp,tt, which is updated in each call to the tracker. The tracker considers a measurement as an OOSM iftd<tt.
When you specify this property as:
'Terminate'
— The tracker stops running when it encounters an out-of-sequence measurement.'Neglect'
— The tracker neglects any out-of-sequence measurements and continues to run.'Retrodiction'
— The tracker uses a retrodiction algorithm to update the tracker by either neglecting the OOSMs, updating existing tracks, or creating new tracks using the OOSM. You must specify a filter initialization function that returns atrackingKF
,trackingEKF
, ortrackingIMM
object in theFilterInitializationFcn
property.
If you specify this property as'Retrodiction'
, the tracker follows these steps to handle the OOSMs:
If the OOSM timestamp is beyond the oldest correction timestamp (specified by the
MaxNumOOSMSteps
property) maintained by the tracker, the tracker discards the OOSMs.If the OOSM timestamp is within the oldest correction timestamp maintained by the tracker, the tracker first retrodicts all the existing tracks to the time of the OOSMs. Then, the tracker applies the joint probability data association algorithm to try to associate the OOSMs to the retrodicted tracks.
If the tracker successfully associates the OOSM to at least one of the retrodicted tracks, then the tracker updates the associated, retrodicted tracks using the OOSMs by applying the retro-correction algorithm to obtain current, corrected tracks.
If the tracker cannot associate an OOSM to any retrodicted track, then the tracker creates a new track based on the OOSM and predicts the track to the current time.
For more details on JPDA-based retrodiction, seeJPDA-Based Retrodiction and Retro-Correction.To simulate out-of-sequence detections, useobjectDetectionDelay
.
Note
When you select
'Retrodiction'
, you cannot use thecostMatrixinput.The benefits of using retrodiction decreases as the number of targets that move in close proximity increases.
The tracker requires all input detections that share the same
SensorIndex
have theirTime
differences bounded by theTimeTolerance
property. Therefore, when you set theOOSMHandlingproperty to'Neglect'
, you must make sure that the out-of-sequence detections have timestamps strictly less than the previous timestamp when running the tracker.
Tunable:Yes
MaxNumOOSMSteps
—Maximum number of out-of-sequence measurement steps
3
(default) |positive integer
Maximum number of out-of-sequence measurement (OOSM) steps, specified as a positive integer.
Increasing the value of this property requires more memory, but enables you to call the tracker with OOSMs that have a larger lag relative to the last timestamp. However, as the lag increases, the impact of the OOSM on the current state of the track diminishes. The recommended value for this property is3
.
Dependencies
To enable this argument, set theOOSMHandling
property to'Retrodiction'
.
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 itsStateParameters
property values to theStateParameters
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
AssignmentThreshold
—Detection assignment threshold
30*[1 Inf]
(default) |positive scalar|1-by-2 vector of positive values
Detection assignment threshold (or gating threshold), specified as a positive scalar or 1-by-2 vector of [C1,C2], whereC1≤C2. If specified as a scalar, the specified value,val, is expanded to [val,Inf
].
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 thanC2. Also, the tracker can only assign a detection to a track if the accurate normalized distance between them is less thanC1. See thedistance
function used with tracking filters (such astrackingCKF
andtrackingEKF
) for explanation of the distance calculation.
Increase the value ofC2if there are track and detection combinations that should be calculated for assignment but are not. Decrease this value if cost calculation takes too much time.
Increase the value ofC1if there are detections that should be assigned to tracks but are not. Decrease this value if there are detections that are assigned to tracks they should not be assigned to (too far away).
Note
If the value ofC2is finite, the state transition function and measurement function, specified in the tracking filter used in the tracker, must be able to take anM-by-Nmatrix of states as input and outputNpredicted states andNmeasurements, respectively.Mis the size of the state.N, the number of states, is an arbitrary nonnegative integer.
DetectionProbability
—Probability of detection
0.9
(default) |scalar in the range [0,1]
Probability of detection, specified as a scalar in the range [0,1]. This property is used in calculations of the marginal posterior probabilities of association and the probability of track existence when initializing and updating a track.
Example:0.85
Data Types:single
|double
InitializationThreshold
—Threshold to initialize a track
0
(default) |scalar in the range [0,1]
The probability threshold to initialize a new track, specified as a scalar in the range [0,1]. If the probabilities of associating a detection with any of the existing tracks are all smaller thanInitializationThreshold
, the detection will be used to initialize a new track. This allows detections that are within the validation gate of a track but have an association probability lower than the initialization threshold to spawn a new track.
Example:0.1
Data Types:single
|double
TrackLogic
—Track confirmation and deletion logic type
'History'
(default) |'Integrated'
Confirmation and deletion logic type, specified as:
'History'
– Track confirmation and deletion is based on the number of times the track has been assigned to a detection in the latest tracker updates.'Integrated'
– Track confirmation and deletion is based on the probability of track existence, which is integrated in the assignment function. Selecting this value enables the joint integrated data association (JIPDA) tracker.
ConfirmationThreshold
—Threshold for track confirmation
scalar|1-by-2 vector
阈值进行跟踪确认,指定为一个年代calar or a 1-by-2 vector. The threshold depends on the type of track confirmation and deletion logic you set with theTrackLogic
property:
'History'
– Specify the confirmation threshold as 1-by-2 vector [MN]. A track is confirmed if it recorded at leastMhits in the lastNupdates. ThetrackerJPDA
registers a hit on a track’s history logic according to theHitMissThrehold
. The default value is(2 3]
.'Integrated'
– Specify the confirmation threshold as a scalar. A track is confirmed if its probability of existence is greater than or equal to the confirmation threshold. The default value is0.95
.
Data Types:single
|double
DeletionThreshold
—Threshold for track deletion
scalar|real-valued 1-by-2 vector
Threshold for track deletion, specified as a scalar or a real-valued 1-by-2 vector. The threshold depends on the type of track confirmation and deletion logic you set with theTrackLogic
property:
'History'
– Specify the confirmation threshold as [PR]. If, inP
of the lastR
tracker updates, a confirmed track is not assigned to any detection that has a likelihood greater than theHitMissThreshold
property, then that track is deleted. The default value is(5,5]
.'Integrated'
– Specify the deletion threshold as a scalar. A track is deleted if its probability of existence drops below the threshold. The default value is0.1
.
Example:0.2
or(5,6]
Data Types:single
|double
HitMissThreshold
—Threshold for registering hit or miss
0.2(default) |scalar in the range [0,1]
Threshold for registering a hit or miss, specified as a scalar in the range [0,1]. The track history logic will register a miss and the track will be coasted if the sum of the marginal probabilities of assignments is below theHitMissThreshold
. Otherwise, the track history logic will register a hit.
Example:0.3
Dependencies
To enable this argument, set theTrackLogic
property to'History'
.
Data Types:single
|double
ClutterDensity
—Spatial density of clutter measurements
1e-6
(default) |positive scalar
Spatial density of clutter measurements, specified as a positive scalar. The clutter density describes the expected number of false positive detections per unit volume. It is used as the parameter of a Poisson clutter model. WhenTrackLogic
is set to'Integrated'
,ClutterDensity
is also used in calculating the initial probability of track existence.
Example:1e-5
Data Types:single
|double
NewTargetDensity
—Spatial density of new targets
1e-5
(default) |positive scalar
Spatial density of new targets, specified as a positive scalar. The new target density describes the expected number of new tracks per unit volume in the measurement space. It is used in calculating the probability of track existence during track initialization.
Example:1e-3
Dependencies
To enable this argument, set theTrackLogic
property to'Integrated'
.
Data Types:single
|double
DeathRate
—Time rate of target deaths
0.01
(default) |scalar in the range [0,1]
Time rate of target deaths, specified as a scalar in the range [0,1].DeathRate
describes the probability with which true targets disappear. It is related to the propagation of the probability of track existence (PTE) :
whereδt上次更新的时间间隔是蒂姆et.
Dependencies
To enable this argument, set theTrackLogic
property to'Integrated'
.
Data Types:single
|double
InitialExistenceProbability
—Initial probability of track existence
0.9
(default) |scalar in the range [0,1]
This property is read-only.
Initial probability of track existence, specified as a scalar in the range [0,1] and calculated asInitialExistenceProbability = NewTargetDensity*DetectionProbability/(ClutterDensity + NewTargetDensity*DetectionProbability)
.
Dependencies
To enable this property, set theTrackLogic
property to'Integrated'
. When theTrackLogic
property is set to'History'
, this property is not available.
Data Types:single
|double
HasCostMatrixInput
—Enable cost matrix input
false
(default) |true
Enable a cost matrix, specified asfalse
ortrue
. Iftrue
, you can provide an assignment cost matrix as an input argument when calling the object.
Data Types:logical
HasDetectableTrackIDsInput
—Enable input of detectable track IDs
false
(default) |true
使检测到的输入追踪id在每个object update, specified asfalse
ortrue
. Set this property totrue
if you want to provide a list of detectable track IDs. This list informs the tracker of all tracks that the sensors are expected to detect and, optionally, the probability of detection for each track.
Data Types:logical
NumTracks
—Number of tracks maintained by tracker
非负整数
This property is read-only.
Number of tracks maintained by the tracker, returned as a nonnegative integer.
Data Types:single
|double
NumConfirmedTracks
—Number of confirmed tracks
非负整数
This property is read-only.
Number of confirmed tracks, returned as a nonnegative integer. If theIsConfirmed
field of an output track structure istrue
, the track is confirmed.
Data Types:single
|double
TimeTolerance
—Absolute time tolerance between detections
1e-5
(default) |positive scalar
Absolute time tolerance between detections for the same sensor, specified as a positive scalar. Ideally,trackerJPDA
expects detections from a sensor to have identical time stamps. However, if the time stamps differences between detections of a sensor are within the margin specified byTimeTolerance
, these detections will be used to update the track estimate based on the average time of these detections.
Data Types:double
EnableMemoryManagement
—Enable memory management properties
false
or0
(default) |true
or1
Enable memory management properties, specified as a logical1
(true
) orfalse
(0
). Setting this property totrue
enables you to use these four properties to specify bounds for certain variable-sized arrays in the tracker, as well as determine how the tracker handles cluster-size violations:
MaxNumDetectionsPerSensor
MaxNumDetectionsPerCluster
MaxNumTracksPerCluster
ClusterViolationHandling
Specifying bounds for variable-sized arrays enables you to manage the memory footprint of the tracker in the generated C/C++ code.
Data Types:logical
MaxNumDetectionsPerSensor
—Maximum number of detections per sensor
100
(default) |positive integer
Maximum number of detections per sensor, specified as a positive integer. This property determines the maximum number of detections that each sensor can pass to the tracker during each call of the tracker.
Set this property to a finite value if you want the tracker to establish efficient bounds on local variables for C/C++ code generation. Set this property toInf
if you do not want to bound the maximum number of detections per sensor.
Dependencies
To enable this property, set theEnableMemoryManagement
property totrue
.
Data Types:single
|double
MaxNumDetectionsPerCluster
—Maximum number of detections per cluster
5
(default) |positive integer
Maximum number of detections per cluster during the run-time of the tracker, specified as a positive integer.
Setting this property to a finite value allows the tracker to bound cluster sizes and reduces the memory footprint of the tracker in generated C/C++ code. Set this property toInf
if you do not want to bound the maximum number of detections per cluster.
If, during run-time, the number of detections in a cluster exceeds the specifiedMaxNumDetectionsPerCluster
, the tracker reacts based on theClusterViolationHandling
property.
Dependencies
To enable this property, set theEnableMemoryManagement
property totrue
.
Data Types:single
|double
MaxNumTracksPerCluster
—Maximum number of tracks per cluster
5
(default) |positive integer
Maximum number of tracks per cluster during the run-time of the tracker, specified as a positive integer.
Setting this property to a finite value enables the tracker to bound cluster sizes and reduces the memory footprint of the tracker in generated C/C++ code. Set this property toInf
if you do not want to bound the maximum number of tracks per cluster.
If, during run-time, the number of tracks in a cluster exceeds the specifiedMaxNumTracksPerCluster
, the tracker reacts based on theClusterViolationHandling
property.
Dependencies
To enable this argument, set theEnableMemoryManagement
property totrue
.
Data Types:single
|double
ClusterViolationHandling
—Handling of run-time violation of cluster bounds
'Split and warn'
(default) |'Terminate'
|'Split'
Handling of run-time violation of cluster bounds, specified as:
'Teminate'
— The tracker reports an error if, during run-time, any cluster violates the cluster bounds specified in theMaxNumDetectionsPerCluster
andMaxNumTracksPerCluster
properties.'Split and warn'
— The tracker splits the size-violating cluster into smaller clusters using a suboptimal approach. The tracker also reports a warning to indicate the violation.'Split'
— The tracker splits the size-violating cluster into smaller clusters by using a suboptimal approach. The tracker does not report a warning.
In the suboptimal approach, the tracker separates out detections or tacks that have the smallest likelihood of association to other tracks or detections until the cluster bounds are satisfied. These separated-out detections or tracks can form one or many new clusters depends on their association likelihoods with each other and theAssignmentThreshold
property.
Dependencies
To enable this property, set theEnableMemoryManagement
property totrue
.
Data Types:char
|string
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 at the update time. Confirmed tracks are corrected and predicted to the update time,confirmedTracks
= tracker(detections
,time
)time
.
also specifies a cost matrix.confirmedTracks
= tracker(detections
,time
,costMatrix
)
To enable this syntax, set theHasCostMatrixInput
property totrue
.
also specifies a list of expected detectable tracks given byconfirmedTracks
= tracker(___,detectableTrackIDs
)detectableTrackIDs
. This argument can be used with any of the previous input syntaxes.
To enable this syntax, set theHasDetectableTrackIDsInput
property totrue
.
(
also returns a list of tentative tracks and a list of all tracks. You can use any of the input arguments in the previous syntaxes.confirmedTracks
,tentativeTracks
,allTracks
] = tracker(___)
(
also returns analysis information that can be used for track analysis. You can use any of the input arguments in the previous syntaxes.confirmedTracks
,tentativeTracks
,allTracks
,analysisInformation
] = tracker(___)
Input Arguments
detections
—Detection list
cell array ofobjectDetection
objects
Detection list, specified as a cell array ofobjectDetection
objects. TheTime
property value of eachobjectDetection
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, theTime
differences between differentobjectDetection
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 largestTime
property value of theobjectDetection
objects in the inputdetections
list.time
must increase in value with each update to the tracker.
Data Types:single
|double
costMatrix
—Cost matrix
real-valuedM-by-Nmatrix
Cost matrix, specified as a real-valuedM-by-Nmatrix, whereMis the number of existing tracks in the previous update, andNis the number of current detections. The cost matrix rows must be in the same order as the list of tracks, and the columns must be in the same order as the list of detections. Obtain the correct order of the list of tracks from the third output argument,allTracks
, when the tracker is updated.
在第一次更新的tracker or when the tracker has no previous tracks, specify the cost matrix to be empty with a size of(0,numDetections]
. Note that the cost must be given 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, you can set the appropriate cost matrix entry toInf
.
Dependencies
To enable this argument, set theHasCostMatrixInput
property totrue
.
Data Types:double
|single
detectableTrackIDs
—Detectable track IDs
real-valuedM-by-1 vector|real-valuedM-by-2 matrix
Detectable track IDs, specified as a real-valuedM-by-1 vector orM-by-2 matrix. Detectable tracks are tracks that the sensors expect to detect. The first column of the matrix contains a list of track IDs that the sensors report as detectable. The optional second column allows you to add the detection probability for each track.
Tracks whose identifiers are not included indetectableTrackIDs
are considered undetectable. In this case, the track deletion logic does not count the lack of detection for that track as a missed detection for track deletion purposes.
Dependencies
To enable this input argument, set thedetectableTrackIDs
property totrue
.
Data Types:single
|double
Output Arguments
confirmedTracks
— Confirmed tracks
array ofobjectTrack
objects | array of structures
Confirmed tracks, returned as an array ofobjectTrack
objects in MATLAB, and returned as an array of structures in code generation. In code generation, the field names of the returned structure are same with the property names ofobjectTrack
.
A track is confirmed if it satisfies the confirmation threshold specified in theConfirmationThreshold
property. In that case, theIsConfirmed
property of the object or field of the structure istrue
.
Data Types:struct
|object
tentativeTracks
— Tentative tracks
array ofobjectTrack
objects | array of structures
Tentative tracks, returned as an array ofobjectTrack
objects in MATLAB, and returned as an array of structures in code generation. In code generation, the field names of the returned structure are same with the property names ofobjectTrack
.
A track is tentative if it does not satisfy the confirmation threshold specified in theConfirmationThreshold
property. In that case, theIsConfirmed
property of the object or field of the structure isfalse
.
Data Types:struct
|object
allTracks
— All tracks
array ofobjectTrack
objects | array of structures
All tracks, returned as an array ofobjectTrack
objects in MATLAB, and returned as an array of structures in code generation. In code generation, the field names of the returned structure are same with the property names ofobjectTrack
. All tracks 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 at the current step of the tracker |
TrackIDsAtStepBeginning |
Track IDs when step began. |
CostMatrix |
Cost matrix for assignment. |
UnassignedTracks |
IDs of unassigned tracks. |
UnassignedDetections |
Indices of unassigned detections in the |
Clusters |
Cell array of cluster reports. |
InitiatedTrackIDs |
IDs of tracks initiated during the step. |
DeletedTrackIDs |
IDs of tracks deleted during the step. |
TrackIDsAtStepEnd |
Track IDs when the step ended. |
MaxNumDetectionsPerCluster |
The maximum number of detections in all the clusters generated during the step. The structure has this field only when you set theEnableMemoryManagement property to'on' . |
MaxNumTracksPerCluster |
The maximum number of tracks in all the clusters generated during the step. The structure has this field only when you set theEnableMemoryManagement property to'on' . |
OOSMHandling |
Analysis information for out-of-sequence measurements handling, returned as a structure. The structure returns this field only when the |
TheClusters
field can include multiple cluster reports. Each cluster report is a structure containing:
Field | Description |
DetectionIndices |
Indices of clustered detections. |
TrackIDs |
Track IDs of clustered tracks. |
ValidationMatrix |
Validation matrix of the cluster. SeejpadEvents for more details. |
SensorIndex |
Index of the originating sensor of the clustered detections. |
TimeStamp |
Mean time stamp of clustered detections. |
MarginalProbabilities |
Matrix of marginal posterior joint association probabilities. |
TheOOSMHandling
structure contains these fields:
Field | Description |
---|---|
DiscardedDetections |
Indices of discarded out-of-sequence detections. An OOSM is discarded if it is not covered by the saved state history specified by theMaxNumOOSMSteps property. |
CostMatrix |
assignm成本ent matrix for the out-of-sequence detections. |
Clusters |
Clusters that are only related to the out-of-sequence detections. |
UnassignedDetections |
Indices of unassigned out-of-sequence detections. The tracker creates new tracks for unassigned out-of-sequence detections. |
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 namedobj
, use this syntax:
release(obj)
Specific totrackerJPDA
predictTracksToTime |
Predict track state |
getTrackFilterProperties |
Obtain track filter properties |
setTrackFilterProperties |
Set track filter properties |
initializeTrack |
Initialize new track |
deleteTrack |
Delete existing track |
exportToSimulink |
Export tracker or track fuser to Simulink model |
Examples
Track Two Objects Using trackerJPDA
Construct atrackerJPDAobject with a default constant velocity Extended Kalman Filter and 'History' track logic. SetAssignmentThreshold100允许共同跟踪相关。
tracker = trackerJPDA('TrackLogic','History','AssignmentThreshold', 100,...'ConfirmationThreshold', [4 5],...'DeletionThreshold', [10 10]);
Specify the true initial positions and velocities of the two objects.
pos_true = [0 0 ; 40 -40 ; 0 0]; V_true = 5*[cosd(-30) cosd(30) ; sind(-30) sind(30) ;0 0];
Create a theater plot to visualize tracks and detections.
tp = theaterPlot('XLimits',[-1 150],'YLimits',[-50 50]); trackP = trackPlotter(tp,'DisplayName','Tracks','MarkerFaceColor','g','HistoryDepth',0); detectionP = detectionPlotter(tp,'DisplayName','Detections','MarkerFaceColor','r');
To obtain the position and velocity, create position and velocity selectors.
positionSelector = [1 0 0 0 0 0; 0 0 1 0 0 0; 0 0 0 0 0 0];% [x, y, 0]velocitySelector = [0 1 0 0 0 0; 0 0 0 1 0 0; 0 0 0 0 0 0 ];% [vx, vy, 0]
Update the tracker with detections, display cost and marginal probability of association information, and visualize tracks with detections.
dt = 0.2;fortime = 0:dt:30% Update the true positions of objects.pos_true = pos_true + V_true*dt;% Create detections of the two objects with noise.detection(1) = objectDetection(time,pos_true(:,1)+1*randn(3,1)); detection(2) = objectDetection(time,pos_true(:,2)+1*randn(3,1));% Step the tracker through time with the detections.(confirmed,tentative,alltracks,info] = tracker(detection,time);% Extract position, velocity and label info.(pos,cov] = getTrackPositions(confirmed,positionSelector); vel = getTrackVelocities(confirmed,velocitySelector); meas = cat(2,detection.Measurement); measCov = cat(3,detection.MeasurementNoise);% Update the plot if there are any tracks.ifnumel(confirmed)>0 labels = arrayfun(@(x)num2str([x.TrackID]),confirmed,'UniformOutput'、假);trackP.plotTrack (pos、或者、浸、标签);enddetectionP.plotDetection(meas',measCov); drawnow;% Display the cost and marginal probability of distribution every eight% seconds.if时间> 0 & & mod(时间,8)= = 0 disp (['At time t = 'num2str(time)' seconds,']);disp('The cost of assignment was: ') disp(info.CostMatrix); disp(['Number of clusters: 'num2str(numel(info.Clusters))]);ifnumel(info.Clusters) == 1 disp('The two tracks were in the same cluster.') disp('Marginal probabilities of association:') disp(info.Clusters{1}.MarginalProbabilities)enddisp('-----------------------------')endend
At time t = 8 seconds,
The cost of assignment was:
1.0 e + 03 * 0.0020 1.1523 1.22770.0053
Number of clusters: 2
-----------------------------
At time t = 16 seconds,
The cost of assignment was:
1.3968 4.5123 2.0747 1.9558
Number of clusters: 1
The two tracks were in the same cluster.
Marginal probabilities of association:
0.8344 0.1656 0.1656 0.8344 0.0000 0.0000
-----------------------------
At time t = 24 seconds,
The cost of assignment was:
1.0e+03 * 0.0018 1.2962 1.2664 0.0013
Number of clusters: 2
-----------------------------
Algorithms
Tracker Logic Flow
When a JPDA tracker processes detections, track creation and management follow these steps.
The tracker divides detections into multiple groups by originating sensor.
For each sensor:
The tracker calculates the distances from detections to existing tracks and forms a
costMatrix
.The tracker creates a validation matrix based on the assignment threshold (or gate threshold) of the existing tracks. A validation matrix is a binary matrix listing all possible detections-to-track associations. For details, seeFeasible Joint Events.
Tracks and detections are then separated into clusters. A cluster can contain one track or multiple tracks if these tracks share common detections within their validation gates. A validation gate is a spatial boundary, in which the predicted detection of the track has a high likelihood to fall. For details, seeFeasible Joint Events.
Update all clusters following the order of the mean detection time stamp within the cluster. For each cluster, the tracker:
Generates all feasible joint events. For details, see
jpdaEvents
.Calculates the posterior probability of each joint event.
Calculates the marginal probability of each individual detection-track pair in the cluster.
Reports weak detections. Weak detections are the detections that are within the validation gate of at least one track, but have probability association to all tracks less than the
IntitializationThreshold
.Updates tracks in the cluster using
correctjpda
.
Unassigned detections (these are not in any cluster) and weak detections spawn new tracks.
The tracker checks all tracks for deletion. Tracks are deleted based on the number of scans without association using
'History'
logic or based on their probability of existence using'Integrated'
track logic.All tracks are predicted to the latest time value (either the time input if provided, or the latest mean cluster time stamp).
Feasible Joint Events
In the typical workflow for a tracking system, the tracker needs to determine if a detection can be associated with any of the existing tracks. If the tracker only maintains one track, the assignment can be done by evaluating the validation gate around the predicted measurement and deciding if the measurement falls within thevalidation gate. In the measurement space, the validation gate is a spatial boundary, such as a 2-D ellipse or a 3-D ellipsoid, centered at the predicted measurement. The validation gate is defined using the probability information (state estimation and covariance, for example) of the existing track, such that the correct or ideal detections have high likelihood (97% probability, for example) of falling within this validation gate.
However, if a tracker maintains multiple tracks, the data association process becomes more complicated, because one detection can fall within the validation gates of multiple tracks. For example, in the following figure, tracksT1andT2are actively maintained in the tracker, and each of them has its own validation gate. Since the detectionD2is in the intersection of the validation gates of bothT1andT2, the two tracks (T1andT2) are connected and form acluster. A cluster is a set of connected tracks and their associated detections.
To represent the association relationship in a cluster, the validation matrix is commonly used. Each row of the validation matrix corresponds to a detection while each column corresponds to a track. To account for the eventuality of each detection being clutter, a first column is added and usually referred to as "Track 0" orT0. If detectionDiis inside the validation gate of trackTj, then the (i,j+1) entry of the validation matrix is 1. Otherwise, it is zero. For the cluster shown in the figure, the validation matrix Ω is
Note that all the elements in the first column of Ω are 1, because any detection can be clutter or false alarm. One important step in the logic of joint probabilistic data association (JPDA) is to obtain all the feasible independent joint events in a cluster. Two assumptions for the feasible joint events are:
A detection cannot be emitted by more than one track.
A track cannot be detected more than once by the sensor during a single scan.
Based on these two assumptions, feasible joint events (FJEs) can be formulated. Each FJE is mapped to an FJE matrix Ωpfrom the initial validation matrix Ω. For example, with the validation matrix Ω, eight FJE matrices can be obtained:
As a direct consequence of the two assumptions, the Ωpmatrices have exactly one "1" value per row. Also, except for the first column which maps to clutter, there can be at most one "1" per column. When the number of connected tracks grows in a cluster, the number of FJE increases rapidly. ThejpdaEvents
function uses an efficient depth-first search algorithm to generate all the feasible joint event matrices.
References
(1] Fortmann, T., Y. Bar-Shalom, and M. Scheffe. "Sonar Tracking of Multiple Targets Using Joint Probabilistic Data Association."IEEE Journal of Ocean Engineering.Vol. 8, Number 3, 1983, pp. 173-184.
(2] Musicki, D., and R. Evans. "Joint Integrated Probabilistic Data Association: JIPDA."IEEE transactions on Aerospace and Electronic Systems .Vol. 40, Number 3, 2004, pp 1093-1099.
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
Usage notes and limitations:
SeeSystem 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.
The tracker supportsstrict single-precisioncode generation with these restrictions:
You must specify the
MaxNumEvents
property as a finite positive integer.You must specify the filter initialization function to return a
trackingEKF
,trackingUKF
,trackingCKF
, ortrackingIMM
object configured with single-precision.
For details, seeGenerate Code with Strict Single-Precision and Non-Dynamic Memory Allocation from Sensor Fusion and Tracking Toolbox.
The tracker supportsnon-dynamic memory allocationcode generation with these restrictions:
You must specify the
MaxNumEvents
property as a finite positive integer.You must specify the filter initialization function to return a
trackingEKF
,trackingUKF
,trackingCKF
, ortrackingIMM
object.You must specify the
MaxNumDetections
property as a finite integer.
For details, seeGenerate Code with Strict Single-Precision and Non-Dynamic Memory Allocation from Sensor Fusion and Tracking Toolbox.
After enabling non-dynamic memory allocation code generation, consider using these properties to set bounds on the local variables in the tracker:
EnableMemeryManagement
MaxNumDetectionsPerSensor
MaxNumDetectionsPerCluster
MaxNumTracksPerCluster
ClusterViolationHandling
Version History
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