Atr Performance Prediction Using Attributed Scattering Features

We present a method for estimating classification performance of a model-based synthetic aperture radar (SAR) automatic target recognition (ATR) system. Target classification is performed by comparing a feature vector extracted from a measured SAR image chip with a feature vector predicted from a hypothesized target class and pose. The feature vectors are matched using a Bayes likelihood metric that incorporates uncertainty in both the predicted and extracted feature vectors. We adopt an attributed scattering center model for the SAR features. The scattering attributes characterize frequency and angle dependence of each scattering center in correspondence the geometry of its physical scattering mechanism. We develop two Bayes matchers that incorporate two different solutions to the problem of correspondence between predicted and extracted scattering centers. We quantify classification performance with respect to the number of scattering center features. We also present classification results when the matchers assume incorrect feature uncertainty statistics.