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 revisit 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.

Date Published: 13 May 2015
PDF: 14 pages
Proc. SPIE 9475, Algorithms for Synthetic Aperture Radar Imagery XXII, 94750H (13 May 2015); doi: 10.1117/12.2176229

Published in SPIE Proceedings Vol. 9475:
Algorithms for Synthetic Aperture Radar Imagery XXII
Edmund Zelnio; Frederick D. Garber, Editor(s)