Bayesian Multiple-Look Updating Applied to the SHARP ATR System
TOYON RESEARCH CORP GOLETA CA
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This study summarizes recent algorithmic enhancements made to the AFRLSNAA Systems-Oriented High Range Resolution HRR Automatic Recognition Program SHARP in the areas of multiple-look updating and sensor fusion. The benefits in improved 1-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 quantified.
- Target Direction, Range and Position Finding
- Statistics and Probability