Pattern classifier and method for associating tracks from different sensors

Abstract

An interceptor-based sensor clusters tracks of objects to generate track clusters based on an uncertainty associated with each track, and generates feature vectors for a cluster under test using the relative placement and the population of other track clusters. The feature vectors may include one or more of a cluster count feature vector (N), a cluster population density feature vector (P), a cluster proximity feature vector (r), a cluster-weighted centroid feature vector (L) and a cluster scattering feature vector (θ). The interceptor-based sensor generates belief functions (μ) from corresponding feature vectors of clusters of tracks generated from a ground-based sensor and the interceptor-based sensor. The interceptor-based sensor may also associate the tracks with a cluster having a track of interest identified by a ground-based sensor based on the belief functions and may select one of the tracks for intercept of a corresponding object within the threat object cloud.