Automatic target recognition using eigen-templates

This paper presents ATR results with High Range Resolution (HRR) profiles used for classification. It is shown that effective HRR-ATR performance can be achieved if the templates are, formed via Singular Value Decomposition (SVD) of detected HRR profiles. It is demonstrated theoretically that in the mean-squared sense, the eigen-vectors represent the optimal feature set. SVD analysis of a large class of XPATCH and MSTAR HRR-data clearly indicates that significant proportion (> 90%) of target energy is accounted for by the eigen-vectors of range correlation matrix, corresponding to only the largest singular value. The SV Decomposition also decouples the range and angle basis spaces. Furthermore, it is shown that significant clutter reduction can be achieved if HRR, data is reconstructed using only the significant eigenvectors. ATR results with eigen-templates are compared with those based on mean-templates. Results are included for both XPATCH and MSTAR data using linear least-squares and matched-filter based classifiers.