Applications of unsupervised clustering algorithms to aircraft identification using high range resolution radar

Abstract

Identification of aircraft from high range resolution (HRR) radar range profiles requires a database of information capturing the variability of the individual range profiles as a function of viewing aspect. This database can be a collection of individual signatures or a collection of average signatures distributed over the region of viewing aspect of interest. An efficient database is one which captures the intrinsic variability of the HRR signatures without either excessive redundancy (over-characterization) typical of single-signature databases or without the loss of information (under-characterization) common when averaging arbitrary group of signatures. The identification of "natural" clustering of similar HRR signatures provides a means for creating efficient databases of either individual signatures or of signature templates. Using a k-means and the Kohonen self-organizing feature net, we identify the natural clustering of the HRR radar range profiles into groups of similar signatures based on the match quality metric (Euclidean distance) used within a Vector quantizer (VQ) classification algorithm. This greatly reduces the redundancy in such databases while retaining classification performance. Such clusters can be useful in template-based algorithms where groups of signatures are averaged to produce a template. Instead of basing the group of signatures to be averaged on arbitrary regions of viewing aspect, the averages are taken over the signatures contained in the natural clusters which have been Identified. The benefits of applying natural cluster identification to individual-signature HRR data preparation are decreased algorithm memory and computational requirements with a consequent decrease in the time required to perform identification calculations. When applied to template databases the benefits are improved identification performance. This paper describes the techniques used for identifying HRR signature clusters and describes the statistical properties of such clusters.