Global Nearest Neighbor Multi Object Tracker

Multi-sensor, multi-object tracker using GNN assignment

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

Description

The Global Nearest Neighbor Multi Object Tracker block is capable of processing detections of many targets from multiple sensors, much like the trackerGNN System object™. The tracker initializes, confirms, predicts, corrects, and deletes tracks based on a global nearest neighbor (GNN) assignment algorithm. The tracker estimates the state vector and state vector covariance matrix for each track. Each detection is assigned to at most one track. If the detection cannot be assigned to any track, the tracker initializes a new 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 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.

Examples

Track Closely Spaced Targets Under Ambiguity in Simulink

Track objects in Simulink® with Sensor Fusion and Tracking Toolbox™ when the association of sensor detections to tracks is ambiguous. It closely follows the Tracking Closely Spaced Targets Under Ambiguity MATLAB® example.

Open Model

Track Simulated Vehicles Using GNN and JPDA Trackers in Simulink

Configure and utilize GNN and JPDA trackers in a simulated highway scenario in Simulink® with Sensor Fusion and Tracking Toolbox™. It closely follows the Sensor Fusion Using Synthetic Radar and Vision Data in Simulink (Automated Driving Toolbox). A main benefit of modeling the system in Simulink is the simplicity of performing "what-if" analysis and choosing a tracker that results in the best performance based on the requirements.

Open Model

Ports

Input

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Detections — Detection list
Simulink^® bus containing MATLAB^® structure

Detection list, specified as a Simulink bus containing a MATLAB structure. The structure has the form:

Field	Description	Type
`NumDetections`	Number of detections	integer
`Detections`	Object detections	Array of object detection structures. The first `NumDetections` of these detections are actual detections.

The fields of Detections are:

Field	Description	Type
`Time`	Measurement time	`single` or `double`
`Measurement`	Object measurements	`single` or `double`
`MeasurementNoise`	Measurement noise covariance matrix	`single` or `double`
`SensorIndex`	Unique ID of the sensor	`single` or `double`
`ObjectClassID`	Object classification ID	`single` or `double`
`MeasurementParameters`	Parameters used by initialization functions of tracking filters	Simulink Bus
`ObjectAttributes`	Additional information passed to tracker	Simulink Bus

See objectDetection for more detailed explanations of these fields.

Note

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

Prediction Time — Track update time
real scalar

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

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

Dependencies

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

Cost Matrix — Cost matrix
real-valued N_t-by-N_d matrix

Cost matrix, specified as a real-valued N_t-by-N_d matrix, where N_t is the number of existing tracks and N_d is the number of current detections.

The rows of the cost matrix correspond to the existing tracks. The columns correspond to the detections. Tracks are ordered as they appear in the list of tracks at the All Tracks output port on the previous invocation of the block.

In the first update to the tracker, or if the track has no previous tracks, assign the cost matrix a size of [0, N_d]. The cost must be calculated so that lower costs indicate a higher likelihood that the tracker assigns a detection to a track. To prevent certain detections from being assigned to certain tracks, use Inf.

If this port is not enabled, the filter initialized by the Filter initialization function calculates the cost matrix using the distance method.

Dependencies

To enable this port, in the Port Setting tab, select Enable cost matrix input.

Detectable TrackIDs — Detectable track IDs
real-valued M-by-1 vector | real-valued M-by-2 matrix

Detectable track IDs, specified as a real-valued M-by-1 vector or M-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 second column contains the detection probability for the track. The detection probability is either reported by a sensor or, if not reported, obtained from the Probability of detection used for track score parameter.

Tracks whose identifiers are not included in Detectable TrackIDs are considered undetectable. The track deletion logic does not count the lack of detection as a "missed detection" for track deletion purposes.

If this port is not enabled, the tracker assumes all tracks to be detectable at each invocation of the block.

Dependencies

To enable this port, in the Port Setting tab, select Enable detectable track IDs Input.

State Parameters — Track state parameters
Simulink bus containing MATLAB structure

Track state parameters, specified as a Simulink bus containing a MATLAB structure. The structure has the form:

Field	Description
`NumParameters`	Number of non-default state parameters, specified as a nonnegative integer
`Parameters`	Array of state parameter structures

The block uses the value of the Parameters field for the StateParameters field 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]`

Dependencies

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

Output

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Confirmed Tracks — Confirmed tracks
Simulink bus containing MATLAB structure

Confirmed tracks, returned as a Simulink bus containing a MATLAB structure. The structure has the form:

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

The fields of the track structure are shown in Track Structure.

Depending on the track logic, a track is confirmed if:

History – A track receives at least M detections in the last N updates. M and N are specified in Confirmation threshold for the History logic.
Score – The track score is at least as high as the confirmation threshold specified in Confirmation threshold for the Score logic.

Tentative Tracks — Tentative tracks
Simulink bus containing MATLAB structure

Tentative tracks, returned as a Simulink bus containing a MATLAB structure. A track is tentative before it is confirmed.

The fields of the track structure are shown in Track Structure.

Dependencies

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

All Tracks — Confirmed and Tentative tracks
Simulink bus containing MATLAB structure

Combined list of confirmed and tentative tracks, returned as a Simulink bus containing a MATLAB structure.

The fields of the track structure are shown in Track Structure.

Dependencies

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

Info — Additional information for analyzing track updates
Simulink bus containing MATLAB structure

Additional information for analyzing track updates, returned as a Simulink bus containing a MATLAB structure.

This table shows the fields of the info structure:

Field	Description
`OOSMDetectionIndices`	Indices of out-of-sequence measurements at the current step of the tracker
`TrackIDsAtStepBeginning`	Track IDs when step 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 from the assignment function, returned as an integer-valued L-by-2 matrix, where L is the number of assignments. The first column of the matrix contains the track IDs and the second column contains the indices of assigned detections. The detection indices are the array-indices of the detections that you used to update the tracker.
`UnassignedTracks`	IDs of unassigned tracks returned from the tracker
`UnassignedDetections`	IDs of unassigned detections returned from the tracker
`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 both the Cluster tracks and detections for assignment and Enable memory management parameters as `on`.
`MaxNumTracksPerCluster`	The maximum number of tracks in all the clusters generated during the step. The structure has this field only when you set both the Cluster tracks and detections for assignment and Enable memory management parameters as `on`.
`OOSMHandling`	Analysis information for out-of-sequence measurements handling, returned as a structure. The structure has this field only when the Out-of-sequence measurements handling parameter of the tracker is specified as `Retordiction`.

The OOSMHandling 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 the Maximum number of OOSM steps parameter.
`CostMatrix`	Cost of assignment matrix for the out-of-sequence measurements
`Assignments`	Assignments between the out-of-sequence detections and the maintained tracks
`UnassignedDetections`	Indices of unassigned out-of-sequence detections. The tracker creates new tracks for unassigned out-of-sequence detections.

Dependencies

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

Parameters

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Tracker Management

Tracker identifier — Unique tracker identifier
`0` (default) | nonnegative integer

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

Example: 1

Filter initialization function — Filter initialization function
`initcvekf` (default) | function name

Filter initialization function, specified as the name of a valid filter initialization function. The tracker uses the filter initialization function when creating new tracks.

Sensor Fusion and Tracking Toolbox™ supplies many initialization functions that you can use:

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.
`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.

You can also write your own initialization function. The function must have this syntax:

filter = filterInitializationFcn(detection)

The input to this function is a detection report like those created by 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

Maximum number of tracks — Maximum number of tracks
`200` (default) | positive integer

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

Maximum number of sensors — Maximum number of sensors
`20` (default) | positive integer

Maximum number of sensors that the block can process, specified as a positive integer. This value should be greater than or equal to the highest SensorIndex value input at the Detections input port.

Out-of-sequence measurements handling — Out-of-sequence measurements handling
`Terminate` (default) | `Neglect` | `Retrodiction`

Out-of-sequence measurements handling, specified as Terminate, Neglect, or Retrodiction. Each detection has an associated timestamp, t_d, and the tracker block has it own timestamp, t_t, which is updated in each invocation. The tracker block considers a measurement as an OOSM if t_d < t_t.

When you specify the parameter as:

Terminate — The block stops running when it encounters an out-of-sequence measurement.
Neglect — The block neglects any out-of-sequence measurements and continues to run.
Retrodiction — The block uses a retrodiction algorithm to update the tracker by either neglecting the OOSM, updating existing tracks, or creating new tracks using the OOSM. You must specify a filter initialization function that returns a trackingKF, trackingEKF, or trackingIMM object in the Filter initialization function parameter.

If you specify this parameter as Retrodiction, the tracker follows these steps to handle the OOSM:

If the OOSM timestamp is beyond the oldest correction timestamp (specified by the Maximum number of OOSM steps parameter) maintained in the tracker, the tracker discards the OOSMs.
If the OOSM timestamp is within the oldest correction timestamp 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 retrodicted track, then the tracker updates the 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 the retrodiction and retro-correction algorithms, see Retrodiction and Retro-Correction. To simulate out-of-sequence detections, use objectDetectionDelay.

Note

When you select Retrodiction, you cannot use the Cost Matrix input.

Maximum number of OOSM steps — Maximum number of OOSM steps
`3` (default) | positive integer

Maximum number of out-of-sequence measurement (OOSMs) steps, specified as a positive integer.

Increasing the value of this parameter requires more memory but allows you to call the tracker block with OOSMs that have a larger lag relative to the last timestamp when the block was updated. Also, as the lag increases, the impact of the OOSM on the current state of the track diminishes. The recommended value of this parameter is 3.

Dependencies

To enable this parameter, set the Out-of-sequence measurements handling parameter to Retrodiction.

Track state parameters — Parameters of track state reference frame
structure | structure array

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

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

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

Data Types: struct

Update track state parameters with time — Update track state parameters with time
`off` (default) | `on`

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

Enable memory management — Enable memory management
`off` (default) | `on`

Select this parameter to enable memory management using the Maximum number of detections per sensor parameter to specify the maximum number of detections that each sensor can pass to the tracker during one call of the tracker. Additionally, if the Cluster tracks and detections for assignment parameter is specified as on, you can use three more parameters to specify bounds for certain variable-sized arrays in the tracker as well as determine how the tracker handles cluster size violations:

Maximum number of detections per cluster
Maximum number of tracks per cluster
Handle run-time violation of cluster size

Specifying bounds for variable-sized arrays allows you to manage the memory footprint of the tracker in the generated C/C++ code.

Assignment algorithm name — Assignment algorithm name
`'MatchPairs'` (default) | `'Munkres'` | `'Jonker-Volgenant'` | `'Auction'` | `'Custom'`

Assignment algorithm, specified as 'MatchPairs', 'Munkres', 'Jonker-Volgenant', 'Auction', or 'Custom'. Munkres is the only assignment algorithm that guarantees an optimal solution, but it is also the slowest, especially for large numbers of detections and tracks. The other algorithms do not guarantee an optimal solution but can be faster for problems with 20 or more tracks and detections. Use'Custom' to define your own assignment function and specify its name in the CustomAssignmentFcn property.

Name of 'Custom' assignment function — Custom assignment function name
character vector

Custom assignment function name, specified as a character string. An assignment function must have this syntax:

 [assignment,unTrs,unDets] = f(cost,costNonAssignment)

For an example of an assignment function and a description of its arguments, see assignmunkres.

Example: 'mycustomfcn'

Dependencies

To enable this property, set the Assignment algorithm name name to 'Custom'.

Threshold for assigning detections to tracks — Threshold for assigning detections to tracks
`30*[1 Inf]` (default) | positive scalar | 1-by-2 vector of positive values

Threshold for assigning detections to tracks (or gating threshold), specified as a positive scalar or a 1-by-2 vector of [C₁,C₂], where C₁ ≤ C₂. If specified as a scalar, the specified value, val, will be 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 than C₂. Also, the tracker can only assign a detection to a track if their accurate normalized distance is less than C₁. See Algorithms for an explanation of the normalized distance.

Increase the value of C₂ 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.
Increase the value of C₁ if there are detections that should be assigned to tracks but are not. Decrease it if there are detections that are assigned to tracks they should not be assigned to (too far away).

Note

If the value of C₂ 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.

Cluster tracks and detections for assignment — Cluster tracks and detections for assignment
`off` (default) | `on`

Specify cluster tracks and detections for assignment as:

off — The tracker solves the global nearest neighbor assignment problem per sensor using a cost matrix. The number of columns in the cost matrix is equal to the number of detections by the sensor, and the number of rows is equal to the number of tracks maintained by the tracker. Forbidden assignments (assignments with a cost greater than the Threshold for assigning detections to tracks parameter) have an infinite cost of assignment.
on — The tracker creates a cluster after separating out the forbidden assignments (assignments with a cost greater than the Threshold for assigning detections to tracks parameter) and uses the forbidden assignments to form new clusters based one the Threshold for assigning detections to tracks parameter. A cluster is a collection of detections (per sensor) and tracks considered to be assigned to each other. In this case, the tracker solves the global nearest neighbor assignment problem per cluster.
When you both specify this property as on and select Enable memory management in the Tracker Management tab, you can use these three parameters to specify bounds for certain variable-sized arrays in the tracker as well as determine how the tracker handles cluster size violations:
- Maximum number of detections per cluster
- Maximum number of tracks per cluster
- Handle run-time violation of cluster size
Specifying bounds for variable-sized arrays enables you to manage the memory footprint of the tracker, especially in the generated C/C++ code.

Simulate using — Type of simulation to run
`Interpreted Execution` (default) | `Code Generation`

Interpreted execution — Simulate the model using the MATLAB interpreter. This option shortens startup time. In Interpreted execution mode, you can debug the source code of the block.
Code generation — Simulate the model using generated C code. The first time you run a simulation, Simulink generates C code for the block. The C code is reused for subsequent simulations as long as the model does not change. This option requires additional startup time.

Track Logic

Type of track confirmation and deletion logic — Confirmation and deletion logic type
`History` (default) | `Score`

Confirmation and deletion logic type, selected as History or Score.

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.
Score – Track confirmation and deletion is based on a log-likelihood track score. A high score means that the track is more likely to be valid. A low score means that the track is more likely to be a false alarm.

Confirmation threshold [M N] — Track confirmation threshold for history logic
`[2 3]` (default) | real-valued 1-by-2 vector of positive integers

Track confirmation threshold for history logic, specified as a real-valued 1-by-2 vector of positive integers [M N]. A track is confirmed if it receives at least M detections in the last N updates.

Dependencies

To enable this parameter, set Type of track confirmation and deletion logic to History.

Deletion threshold [P Q] — Track deletion threshold for history logic
`[5 5]` (default) | real-valued 1-by-2 vector of positive integers

Track deletion threshold for history logic, specified as a real-valued 1-by-2 vector of positive integers [P Q]. If a confirmed track is not assigned to any detection P times in the last Q tracker updates, then the track is deleted.

Dependencies

To enable this parameter, set Type of track confirmation and deletion logic to History.

Confirmation threshold [positive scalar] — Track confirmation threshold for score logic
`20` (default) | positive scalar

Track confirmation threshold for score logic, specified as a real-valued positive scalar. A track is confirmed if its score is at least as high as the confirmation threshold.