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Multinomial pattern matching (MPM) is an automatic target recognition algorithm developed for specifically radar data at Sandia National Laboratories. The algorithm is in a family of algorithms that first quantizes pixel value into N q bins based on pixel amplitude before training and classification. This quantization step reduces the sensitivity of algorithm performance to absolute intensity variation in the data, typical of radar data where signatures exhibit high variation for even small changes in aspect angle. Our previous work has focused on performance analysis of peaky template matching, a special case of MPM where binary quantization is used (N q = 2). Unfortunately references on these algorithms are generally difficult to locate and here we re-visit the MPM algorithm and illustrate the underlying statistical model and decision rules for two algorithm interpretations: the 1-of-K vector form and the scalar. MPM can also be used as a detector and specific attention is given to algorithm tuning where "peak pixels" are chosen based on their underlying empirical probabilities according to a reward minimization strategy aimed at reducing false alarms in the detection scenario and false positives in a classification capacity. The algorithms are demonstrated using Monte Carlo simulations on the AFRL civilian vehicle dataset for variety of choices of N q .

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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 N q allowing the performance loss under IPR variations to be mapped to an equivalent loss in signal-to-noise ratio.