A robust similarity measure for attributed scattering center sets with application to SAR ATR

This paper proposes a robust similarity measure for two attributed scattering center (ASC) sets and applies it to synthetic aperture radar (SAR) automatic target recognition (ATR). The extraction uncertainty of an individual ASC is modeled by an adaptive Gaussian distribution according to its attributes. Then the distance between two individual ASCs is defined as the Kullback-Leibler (KL) divergence between two Gaussian distributions which model the uncertainties of those two ASCs. The proposed distance measure can better exploit the inner discrepancy between individual ASCs compared with the Euclid distance or Mahalanobis distance. Based on the proposed distance measure, a cost matrix which contains the costs of false and missing ASCs is built and the Hungarian algorithm is employed to build a one-to-one correspondence between two ASC sets. A threshold method is carried out to further evaluate the Hungarian assignment. Afterwards, a robust similarity measure is designed to evaluate the similarity between the two ASC sets which comprehensively considers the influences of the missing and false ASCs as well as the disproportionate contributions by different ASCs. Finally, the target type is determined by the similarities between the testing image and various types of template targets. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) dataset verify the validity and robustness of the proposed method.