ctrectmeas

Constant turn-rate rectangular target measurement model

Syntax

measurements = ctrectmeas(states,detections)

Description

measurements = ctrectmeas(states,detections) returns the expected measurements from the current rectangular states and detections.

Examples

Expected Detections Using Rectangular Measurement Model

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);

Input Arguments

states — Current rectangular states

7-by-N real-valued matrix

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]:

Variable	Meaning	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 — Detections of target

1-by-M cell array of objectDetection objects

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

measurements — Expected measurements

P-by-N-by-M real-valued array

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™.