Robust SAR ATR via set-valued classifiers: new results

“Robust identification” in SAR ATR refers to the problem of determining target identity despite the confounding effects of “extended operating conditions” (EOCs). EOC’s are statistically uncharacterizable SAR intensity-signature variations caused by mud, dents, turret articulations, etc. This paper describes a robust ATR approach based on the idea of (1) hedging against EOCs by attaching “random error bars” (random intervals) to each value of the image likelihood function; (2) constructing a “generalized likelihood function” from them; and (3) using a set-valued, MLE-like approach to robustly estimate target type. We compare three such classifiers, showing that they outperform conventional approaches under EOC conditions.