Shadow-based SAR ATR performance prediction

The ability to assess potential automatic target recognition (ATR) performance for a given SAR system, target set and clutter environment is a key requirement for system procurement and mission planning. A cost-effective solution is to develop a theoretical model which can provide ATR performance predictions given a parameterisation of the system, targets and environment. In this paper, a classification scheme based on shadow information is analysed. Consideration of the statistical accuracy of shadow-based features allows ATR performance to be predicted. Quantitative comparisons of predicted performance with results obtained via simulation as well as against real data from the MSTAR data set are presented. It is seen that a reasonable level of agreement is obtained which gives confidence in extending the theoretical concepts to more complex feature-based ATR schemes.