rng(2021) % For repeatable results
x1 = [5; 5; 0] + randn(3,4); % Four detections in cluster one
x2 = [5; -5; 0] + randn(3,4); % Four detections in cluster two
xFalse = 30*randn(3,2); % Two false alarms
x = [x1 x2 xFalse];

detections = repmat({objectDetection(0,[0; 0; 0])},10,1);
for i = 1:10
    detections{i}.Measurement = x(:,i);
end

clusterIndex = [1; 1; 1; 1; 2; 2; 2; 2; 3; 4];

clusteredDetections = mergeDetections(detections,clusterIndex);

% Create a theaterPlot object.
tp = theaterPlot;

% Create two detection plotters, one for unclustered detections and one for
% clustered detections.
detPlotterUn = detectionPlotter(tp,DisplayName="Unclustered Detections", ...
    MarkerFaceColor="b",MarkerEdgeColor="b");
detPlotterC = detectionPlotter(tp,DisplayName="Clustered Detections", ...
    MarkerFaceColor="r",MarkerEdgeColor="r");

% Concatenate measurements and covariances for unclustered detections
detArray = [detections{:}];
xUn = horzcat(detArray.Measurement)';
PUn = cat(3,detArray.MeasurementNoise);

% Concatenate measurements and covariance for clustered detections
clusteredDetArray = [clusteredDetections{:}];
xC = horzcat(clusteredDetArray.Measurement)';
PC = cat(3,clusteredDetArray.MeasurementNoise);

% Plot all unclustered and clustered detections
plotDetection(detPlotterUn,xUn,PUn);
plotDetection(detPlotterC,xC,PC);