Cluster Detections in Range and Doppler
Create detections of extended objects with measurements in range and Doppler. Assume the maximum unambiguous range is 20 m and the unambiguous Doppler span extends from Hz to Hz. Data for this example is contained in the
dataClusterDBSCAN.mat file. The first column of the data matrix represents range, and the second column represents Doppler.
The input data contains the following extended targets and false alarms:
an unambiguous target located at
an ambiguous target in Doppler located at
an ambiguous target in range located at
an ambiguous target in range and Doppler located at
5 false alarms
clusterDBSCAN object and specify that disambiguation is not performed by setting
false. Solve for the cluster indices.
load('dataClusterDBSCAN.mat'); cluster1 = clusterDBSCAN('MinNumPoints',3,'Epsilon',2, ... 'EnableDisambiguation',false); idx = cluster1(x);
plot object function to display the clusters.
The plot indicates that there are eight apparent clusters and six noise points. The '
Dimension 1' label corresponds to range and the '
Dimension 2' label corresponds to Doppler.
Next, create another
clusterDBSCAN object and set
true to specify that clustering is performed across the range and Doppler ambiguity boundaries.
cluster2 = clusterDBSCAN('MinNumPoints',3,'Epsilon',2, ... 'EnableDisambiguation',true,'AmbiguousDimension',[1 2]);
Perform the clustering using ambiguity limits and then plot the clustering results. The DBSCAN clustering results correctly show four clusters and five noise points. For example, the points at ranges close to zero are clustered with points near 20 m because the maximum unambiguous range is 20 m.
amblims = [0 maxRange; minDoppler maxDoppler]; idx = cluster2(x,amblims); plot(cluster2,x,idx)
clusterer — Clusterer object
Clusterer object, specified as a
X — Input data to cluster
real-valued N-by-P matrix
Input data, specified as a real-valued N-by-P matrix. The N rows correspond to points in a P-dimensional feature space. The P columns contain the values of the features over which clustering takes place. For example, a two-column input can contain Cartesian coordinates x and y, or range and Doppler.
idx — Cluster indices
N-by-1 integer-valued column vector
Cluster indices, specified as an N-by-1 integer-valued column
vector. Cluster indices represent the clustering results of the DBSCAN algorithm
contained in the first output argument of
idx values start at one and are consecutively numbered. The plot
object function labels each cluster with the cluster index. A value of –1 in
idx indicates a DBSCAN noise point. Noise points are not
ax — Axes of plot
Axes of plot, specified as an
Axes object handle.
titlestr — Plot title
character vector | string
Plot title, specified as a character vector or string.
fh — Figure handle of plot
Figure handle of plot, returned as a positive scalar.