Improved automatic target recognition using singular value decomposition

A new algorithm is presented for automatic target recognition (ATR) where the templates are obtained via singular value decomposition (SVD) of high range resolution (HRR) profiles. SVD analysis of a large class of HRR data reveals that the range-space eigenvectors corresponding to the largest singular value accounts for more than 90% of the target energy. Hence, it is proposed that the range-space eigenvectors be used as templates for classification. The effectiveness of data normalization and Gaussianization of profile data for improved classification performance is also studied. With extensive simulation studies it is shown that the proposed eigen-template based ATR approach provides consistent superior performance with the recognition rate reaching 99.5% for the four class XPATCH database