Performance prediction of multinomial pattern matching under ideal point response variations

Typical ATR performance metrics are based on the results of empirical studies on truthed datasets where it is difficult to fully sample the space of expected variation yielding potentially false generalizations of empirical performance results to a rigorous performance assessment. This is especially difficult when many sources of variation can exist in the data, typically referred to as operating conditions. Here, we propose a general method to analytically predict the classification performance of the MPM algorithm when samples are assumed realizations of two separate MPM template parametrizations differing as a function of a single, conditionally independent operation condition. This performance prediction approach is then used to investigate the role the ideal point response has in the classification performance of synthetic aperture radar targets. The exact trade-off we study is coherently processing an aperture to yield a single higher resolution image versus non-coherently processing the aperture to yield multiple lower resolution looks of a scene. Experiments are conducted using SAR imagery from the Air Force Research Laboratories Civilian Vehicle dataset. An additional performance analysis presents an analytic approach to predict algorithm performance under additive white Gaussian noise for a general Nq allowing the performance loss under IPR variations to be mapped to an equivalent loss in signal-to-noise ratio.