Bayesian multiple-look updating applied to the SHARP ATR system

This study summarizes recent algorithmic enhancements made to the AFRL/SNAA Systems-Oriented High Range Resolution (HRR) Automatic Recognition Program (SHARP) in the areas of multiple-look updating and sensor fusion. The benefits in improved I-D Automatic Target Recognition (ATR) performance resulting from these enhancements are quantified. The study incorporates a unique method of estimating Bayesian probabilities by exploiting the fact that 1-D range profiles formed from Moving and Stationary Target Acquisition and Recognition (MSTAR) target chips overlap in azimuth. Thus, multiple samples of range profiles exist for the same target at very similar viewing aspects, but from independent passes of the sensor. ATR performance using the Bayesian technique is characterized first for an updating architecture that fuses probabilities over a fixed number of looks and then makes a "classify or reject" decision. A second proposed architecture that makes a "classify, reject, or take another measurement" decision is also analyzed. For both postulated architectures, ATR performance enhancement over the SHARP baseline updating procedure is quanitified.