mergeDetections
Syntax
Description
clusteredDetections = mergeDetections(detections,clusterIndex)Time,
SensorIndex, ObjectClassID,
MeasurementParameters, and ObjectAttributes
properties or fields.
clusteredDetections = mergeDetections(___,MergingFcn=mergeFcn)
Examples
Generate two clusters of detections with two false alarms.
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];
Format these detections into a cell array of objectDetection objects.
detections = repmat({objectDetection(0,[0; 0; 0])},10,1);
for i = 1:10
detections{i}.Measurement = x(:,i);
endDefine the cluster indices according to the previously defined scenario. You can typically obtain the cluster indices by applying a clustering algorithm on the detections.
clusterIndex = [1; 1; 1; 1; 2; 2; 2; 2; 3; 4];
Use the mergeDetections function to merge the detections.
clusteredDetections = mergeDetections(detections,clusterIndex);
Visualize the results in a theater plot.
% 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);
