Performance-Complexity Tradeoffs for Several Approaches to ATR from SAR Images

The performance of an automatic target recognition (ATR) system for synthetic aperture radar (SAR) images is generally dependent upon a set of parameters which captures the assumptions and approximations made in the implementation of the system. This set of parameters implicitly or explicitly determines a level of database complexity for the system. A comprehensive analysis of the empirical tradeoffs between ATR performance and database complexity is presented for variations of several algorithms including a likelihood approach under a conditionally Gaussian model for pixel distribution, a mean squared error classifier on pixel dB values, and a mean squared error classifier on pixel quarter power values. These algorithms are applied under a common framework to identical training and testing sets of SAR images for a wide range of system parameters. Their performance is characterized both in terms of the percentage of correctly classified test images and the aver-age squared Hilbert-Schmidt distance between the estimated and true target orientations across all test images. Performance boundary curves are presented and compared, and algorithm performance is detailed at key com-plexity values. For the range of complexity considered, it is shown that in terms of target orientation estimation the likelihood based approach under a conditionally Gaussian model yields superior performance for any given database complexity than any of the other aproaches tested. It is also shown that some variant of each of the approaches tested delivers superior target classification performance over some range of complexity