staticDetectionFuser

Static fusion of synchronous sensor detections

Description

staticDetectionFuser System object™ creates a static detection fuser object to fuse angle-only sensor detections.

To obtain the fuser:

1. Create the staticDetectionFuser object and set its properties.

2. 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

Syntax

fuser = staticDetectionFuser()

fuser = staticDetectionFuser(Name,Value)

Description

fuser = staticDetectionFuser() creates a default three-sensor static detection fuser object to fuse angle-only sensor detections.

fuser = staticDetectionFuser(Name,Value) sets properties using one or more name-value pairs. For example, fuser = staticDetectionFuser('FalseAlarmRate',1e-6,'MaxNumSensors',12) creates a fuser that has a maximum of 12 sensors and a false alarm rate of 1e-6. 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.

FuserSensorIndex — Sensor index of composite detections

1 (default) | positive integer

Sensor index of the composite detections reported by the fuser, specified as a positive integer. This index becomes the SensorIndex of objectDetection objects returned by the fuser.

Example: 5

Data Types: double

MeasurementFusionFcn — Function for fusing multiple sensor detections

'triangulateLOS' (default) | char | string | function handle

Function for fusing multiple sensor detections, specified as a character vector, string, or function handle. The function fuses multiple detections into one and returns the fused measurement and measurement noise. Any fusing function combines at most one detection from each sensor. The syntax of the measurement fuser function is:

[fusedMeasurement,fusedMeasurementNoise] = MeasurementFusionFcn(detections)

where the input and output functions arguments are

• detections – cell array of objectDetection measurements.

• fusedMeasurement – an N-by-1 vector of fused measurements.

• fusedMeasurementNoise – an N-by-N matrix of fused measurements noise.

The value of N depends on the MeasurementFormat property.

MeasurementFormat Property	N
'Position'	1, 2, and 3
'Velocity	1, 2, and 3
'PositionAndVelocity	2, 4, and 6
'Custom'	Any

Data Types: char | string | function_handle

MeasurementFormat — Format of the fused measurement

'Position' (default) | 'Velocity' | 'PositionAndVelocity' | 'Custom'

Format of the fused measurement, specified as 'Position', 'Velocity', 'PositionAndVelocity', or 'Custom'. The formats are

• 'Position' – the fused measurement is the position of the target in the global coordinate frame.

• 'Velocity' – the fused measurement is the velocity of the target in the global coordinate frame.

• 'PositionAndVelocity' – the fused measurement is the position and velocity of the target in the global coordinate frame defined according to the format [x;vx;y;vy;z;vz].

• 'Custom' – custom fused measurement. To enable this format, specify a function using the MeasurementFcn.

Example: 'PositionAndVelocity'

MeasurementFcn — Custom measurement function

char | string | function handle

Custom measurement function, specified as a character vector, string, or function handle. Specify the function that transforms fused measurements into sensor measurements. The function must have the following signature:

sensorMeas = MeasurementFcn(fusedMeas,measParameters)

Dependencies

To enable this property, set the MeasurementFormat property to 'Custom'.

Data Types: char | string | function_handle

MaxNumSensors — Maximum number of sensors in surveillance region

3 (default) | positive integer greater than one

Maximum number of sensors in surveillance region, specified as a positive integer greater than one.

Data Types: double

Volume — Volume of sensor detection bin

1e-2 (default) | positive scalar | N-length vector of positive scalars

Volume of sensors detection bins, specified as a positive scalar or N-length vector of positive scalars. N is the number of sensors. If specified as a scalar, each sensor is assigned the same volume. If a sensor produces an angle-only measurement, for example, azimuth and elevation, the volume is defined as the solid angle subtended by one bin.

Data Types: double

DetectionProbability — Probabilities of a target detection

0.9 (default) | positive scalar | N-length vector of positive scalars

Probability of detection of a target by each sensor, specified as a scalar or N-length vector of positive scalars in the range (0,1). N is the number of sensors. If specified as a scalar, each sensor is assigned the same detection probability. The probability of detection is used in calculating the cost of fusing a "one" (target was detected) or "zero" (target was not detected) detections from each sensor.

Example: 0.99

Data Types: double

FalseAlarmRate — Rate of false positives generated by sensors

1e-6 (default) | positive scalar | N-length vector of positive scalars

Rate at which false positives are reported by sensor in each bin, specified as a scalar or N-length vector of positive scalars. N is the number of sensors. If specified as a scalar, each sensor is assigned the same false alarm rate. The false alarm rate is used to calculate the likelihood of clutter in the detections reported by each sensor.

Example: 1e-5

Data Types: double

UseParallel — Option to use parallel computing resources

false (default) | true

Option to use parallel computing resources, specified as false or true. The staticDetectionFuser calculates the cost of fusing detections from each sensor as an n-D assignment problem. The fuser spends most of the time in computing the cost matrix for the assignment problem. If Parallel Computing Toolbox™ is installed, this option lets the fuser use the parallel pool of workers to compute the cost matrix.

Data Types: logical

TimeTolerance — Absolute tolerance between timestamps of detections

1e-6 (default) | nonnegative scalar

Absolute tolerance between timestamps of detections, specified as a nonnegative scalar. The staticDetectionFuser assumes that sensors are synchronous. This property defines the allowed tolerance value between detection time-stamps to still be considered synchronous.

Example: 1e-3

Data Types: double

Usage

Syntax

compositeDets = fuser(dets)

[compositeDets,analysisInfo] = fuser(dets)

Description

compositeDets = fuser(dets) returns the fused detections, compositeDets, of input detections, dets.

[compositeDets,analysisInfo] = fuser(dets) also returns analysis information, analysisInfo.

Input Arguments

dets — Pre-fused detections

cell array of objectDetection objects

Pre-fused detections, specified as a cell array of objectDetection objects.

Output Arguments

compositeDets — Fused detections

cell array of objectDetection objects

Pre-fused detections, returned as a cell array of objectDetection objects.

analysisInfo — Analysis information

structure

Analysis information, returned as a structure. The fields of the structure are:

• CostMatrix – N-dimensional cost matrix providing the cost of association of detections, where N is the number of sensors. The cost is the negative log-likelihood of the association and can be interpreted as the negative score of the track that will be generated by the fused measurement.

• Assignments – A P-by-N list of assignments, where P is the number of composite detections.

• FalseAlarms – A Q-by-1 list of indices of detections declared as false alarms by association.

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)

Common to All System Objects

release	Release resources and allow changes to System object property values and input characteristics
reset	Reset internal states of System object
isLocked	Determine if System object is in use
clone	Create duplicate System object

Examples

Fuse Detections from ESM Sensors

Fuse angle-only detections from three ESM sensors.

Load stored detections from the sensors.

load('angleOnlyDetectionFusion.mat','detections');

Visualize angle-only detections for plotting the direction vector.

rPlot = 5000;
plotData = zeros(3,numel(detections)*3);
for i = 1:numel(detections)
    az = detections{i}.Measurement(1);
    el = detections{i}.Measurement(2);
    [xt,yt,zt] = sph2cart(deg2rad(az),deg2rad(el),rPlot);
    % The sensor is co-located at platform center, therefore use
    % the position from the second measurement parameter
    originPos = detections{i}.MeasurementParameters(2).OriginPosition;
    positionData(:,i) = originPos(:);
    plotData(:,3*i-2) = [xt;yt;zt] + originPos(:);
    plotData(:,3*i-1) = originPos(:);
    plotData(:,3*i) = [NaN;NaN;NaN];
end
plot3(plotData(1,:),plotData(2,:),plotData(3,:),'r-')
hold on
plot3(positionData(1,:),positionData(2,:),positionData(3,:),'o','MarkerSize',12,'MarkerFaceColor','g')

Create a staticDetectionFuser to fuse angle-only detections using the measurement fusion function triangulateLOS.

fuser = staticDetectionFuser('MeasurementFusionFcn','triangulateLOS','MaxNumSensors',3)

fuser = 
  staticDetectionFuser with properties:

        FusedSensorIndex: 1
    MeasurementFusionFcn: 'triangulateLOS'
       MeasurementFormat: 'Position'

           MaxNumSensors: 3
                  Volume: [3×1 double]
    DetectionProbability: [3×1 double]
          FalseAlarmRate: [3×1 double]

           TimeTolerance: 1.0000e-06
             UseParallel: false

Create the fused detections and obtain the analysis information.

[fusedDetections, analysisInfo] = fuser(detections);
fusedPositions = zeros(3,numel(fusedDetections));
for i = 1:numel(fusedDetections)
    fusedPositions(:,i) = fusedDetections{i}.Measurement;
end
plot3(fusedPositions(1,:),fusedPositions(2,:),fusedPositions(3,:),'ko', ...
    'MarkerSize',12, 'MarkerFaceColor','k')
legend('Angle-only Detections','Sensor Positions','Fused Target Measurements')
title('Angle-only Detection Fusion')
xlabel('x [m]')
ylabel('y [m]')
view(2)

Use the analysisInfo output to check the assignments.

analysisInfo.Assignments

ans = 6×3 uint32 matrix

    0   10   14
    1    6   11
    2    7   12
    3    8   13
    4    9    0
    5    0   15

Algorithms

Detection Fusion Workflow

The static detection fuser:

• Calculates the cost of fusing or matching detections from each sensor to one another.

• Solves a 2-D or S-D assignment problem, where S is the number of sensors, to associate or match detections from one sensor to others.

• Fuses the measurement and measurement covariance of the associated detection n-tuples to generate a list of composite or fused detections.

• Declares unassigned detections from each sensor as false alarms.

The staticDetectionFuser assumes that all sensors are synchronous and generate detections simultaneously. The staticDetectionFuser also assumes that the sensors share a common surveillance region. Associating n detections from m sensors indicates m - n missed detections or false alarms.

References

[1] Bar-Shalom, Yaakov, Peter K. Willett, and Xin Tian. Tracking and data fusion. Storrs, CT, USA:: YBS publishing, 2011.

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).

Version History

Introduced in R2018b