Date of Award
Master of Science
Department of Electrical and Computer Engineering
Steven K. Rogers, PhD
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 typical of single-signature databases, or without the loss of information common when averaging arbitrary groups 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 used within a Vector Quantizer 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 containing intake natural clusters which have been identified.
DTIC Accession Number
Pham, Dzung Tri, "Applications of Unsupervised Clustering Algorithms to Aircraft Identification Using High Range Resolution Radar" (1997). Theses and Dissertations. 5620.