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ctrectmeas

Measurement function of constant turn-rate motion model for rectangular targets

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Syntax

measurements = ctrectmeas(states,detections)

Description

measurements = ctrectmeas(states,detections) returns the expected measurements from the current rectangular states and detections for an extended target based on the constant turn-rate motion model. The gmphd object can use ctrectmeas as its MeasurementFcn.

example

Examples

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Open Live Script

Load detections and truth generated from a rectangular target.

load('rectangularTargetDetections.mat','detections','truthState');

Generate expected detections from the target's rectangular state and actual detections using ctrectmeas.

tgtState = [3;48;0;60;0;5;1.9];
zExp = ctrectmeas(tgtState,detections);

Set up visualization environment using theaterPlot.

theaterP = theaterPlot;
stateP = trackPlotter(theaterP,'DisplayName','State','MarkerFaceColor','g');
truthP = trackPlotter(theaterP,'DisplayName','Truth','MarkerFaceColor', 'b');
detP = detectionPlotter(theaterP,'DisplayName','Detections','MarkerFaceColor','r');
expDetP = detectionPlotter(theaterP,'DisplayName','Expected Detections','MarkerFaceColor','y');
l = legend(theaterP.Parent);
l.AutoUpdate = 'on';
hold on;
assignP = plot(theaterP.Parent,NaN,NaN,'-.','DisplayName','Association');

Plot actual and expected detections.

inDets = [detections{:}];
inMeas = horzcat(inDets.Measurement);
detP.plotDetection(inMeas');

zExpPlot = reshape(zExp,3,[]);
expDetP.plotDetection(zExpPlot');

Plot association lines.

zLines = nan(2,numel(detections)*3);
zLines(1,1:3:end) = zExpPlot(1,:);
zLines(2,1:3:end) = zExpPlot(2,:);
zLines(1,2:3:end) = inMeas(1,:);
zLines(2,2:3:end) = inMeas(2,:);
assignP.XData = zLines(1,:);
assignP.YData = zLines(2,:);

Plot truth and state.

truthPos = [truthState(1:2);0];
truthDims = struct('Length',truthState(6),...
    'Width',truthState(7),...
    'Height', 0,...
    'OriginOffset', [0 0 0]);
truthOrient = quaternion([truthState(4) 0 0],'eulerd', 'ZYX','frame');
truthP.plotTrack(truthPos',truthDims,truthOrient);

statePos = [tgtState(1:2);0];
stateDims = struct('Length',tgtState(6),...
    'Width',tgtState(7),...
    'Height',0,...
    'OriginOffset', [0 0 0]);
stateOrient = quaternion([tgtState(4) 0 0],'eulerd', 'ZYX','frame');
stateP.plotTrack(statePos', stateDims, stateOrient);

Figure contains an axes object. The axes object with xlabel X (m), ylabel Y (m) contains 5 objects of type line. One or more of the lines displays its values using only markers These objects represent State, Truth, Detections, Expected Detections, Association.

Input Arguments

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Current rectangular states, specified as a 7-by-N real-valued matrix, where N is the number of states. The seven-dimensional rectangular target state is defined as [x; y; s; θ; ω; L; W]:

VariableMeaning Unit
x

Position of the rectangle center in x direction

m
y

Position of the rectangle center in y direction

m
s

Speed in the heading direction

m/s
θ

Orientation angle of the rectangle with respect to x direction

degree
ω

Turn-rate

degree/s
L

Length of the rectangle

m
W

Width of the rectangle

m

Example: [1;2;2;30;1;4.7;1.8]

Data Types: single | double

Detections of target, specified as a 1-by-M cell array of objectDetection objects. The MeasurementParameters property (that specifies the transformation from the state-space to measurement-space) for each object must be the same for all the detections in the cell array.

Output Arguments

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Expected measurements, returned as a P-by-N-by-M real-valued array. P is the dimension of each measurement specified in the detections input, N is the number of states specified in the states input, and M is the number of detections specified in the detections input.

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

Version History

Introduced in R2019b

See Also

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