mergeDetections
Syntax
Description
merges detections sharing the same cluster labels. By default, the function merges
detections in the same cluster using a Gaussian mixture merging algorithm. The function
assumes that all detections in the same cluster share the same clusteredDetections
= mergeDetections(detections
,clusterIndex
)Time
,
SensorIndex
, ObjectClassID
,
MeasurementParameters
, and ObjectAttributes
properties or fields.
specifies the function used to merge the detections in addition to the input arguments from
the previous syntax.clusteredDetections
= mergeDetections(___,MergingFcn=mergeFcn
)
Examples
Merge Detections to Generate Clustered Detections
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); end
Define 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);
Input Arguments
detections
— Object detections
N-element array of objectDetection
objects | N-element cell array of objectDetection
objects | N-element array of structures
Object detections, specified as an N-element array of objectDetection
objects, N-element cell array of
objectDetection
objects, or an N-element array of
structures whose field names are the same as the property names of the
objectDetection
object. N is the number of
detections. You can create detections
directly, or you can obtain
detections
from the outputs of sensor objects such as fusionRadarSensor
, irSensor
, and
sonarSensor
.
clusterIndex
— Cluster indices
N-element vector of positive integers
Cluster indices, specified as an N-element vector of positive
integers, where N is the number of detections specified in the
detections
input. Each element is the cluster index of the
corresponding detection in the detections
input. For example, if
clusterIndex(i)=k
, then the i
th detection from
the detections
input belongs to cluster k
.
mergeFcn
— Function to merge detections
function handle
Function to merge detections, specified as a function handle. You must use one of these syntaxes to define the function:
Syntax with detection input and output:
detectionOut = mergeFcn(detectionsIn)
where:
detectionsIn
is specified as a cell array ofobjectDetection
objects (in the same cluster).detectionOut
is returned as anobjectDetection
object.
Syntax with state mean and covariance input and output:
[mergedMean,mergedCovariance] = mergeFcn(means,covariances)
where:
means
is specified as an M-by-Q matrix, representing measurements in the cluster. M is the size of each measurement and Q is the number of measurements in the cluster.covariances
is specified an M-by-M-by-Q matrix, representing the uncertainty covariance matrices corresponding tomeans
. M is the size of each measurement and Q is the number of measurements in the cluster.mergedMean
is returned a P-by-1 vector, representing the merged measurement. Note that the size of the merged measurement (P) can be different from the size of the input measurement (M). This enables you to merge detections into parameterized forms, such as rectangular or cuboid detections.mergedCovariance
is returned as a P-by-P matrix, representing the uncertainty covariance in the merged measurement. P is the size of the merged mean.
Tip
You can use built-in functions, such as fusecovint
, fusecovunion
, and fusexcov
, as the merging function.
Example: @mergeFcn
Output Arguments
clusteredDetections
— Clustered detections
M-element cell array of objectDetection
objects
Clustered detections, returned as an M-element cell array of
objectDetection
objects, where
M is the number of unique cluster indices specified in the
clusterIndex
input.
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
The function supports non-dynamic memory allocation code generation. For details, see Generate Code with Strict Single-Precision and Non-Dynamic Memory Allocation.
The function supports strict single-precision code generation. For details, see Generate Code with Strict Single-Precision and Non-Dynamic Memory Allocation.
Version History
Introduced in R2021bR2022b: Specify measurement means and covariances inputs
When you specify the MergingFcn
name-value argument, you can now
also specify measurement means and covariances as inputs to the merging function.
Previously, you could use only objectDetection
objects or its equivalent structures as
inputs.
See Also
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