Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
Clustering Algorithms
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Output data from many types of sensor systems (radar, radar warning, sonar, electro-optical, etc.) must be associated with one or more possible sources based on multiple observations of the data. This paper presents an algorithm that associates data with their source by simultaneous n-dimensional clustering of multiple data observations. The algorithm first orders the observations by successive nearest neighbor, in the n-dimensional Euclidean sense, from a defined starting point. Clusters are then isolated using a method derived from statistical decision theory. The algorithm's primary feature is its ability to perform clustering adaptively without any assumptions about the size, number, or statistical characteristics of the clusters. Since the algorithm was developed for radar warning system processing, a performance comparison with a well-known algorithm used in that field is included.