Multinomial pattern matching for high range resolution radar profiles

Airborne ground moving-target indication (GMTI) radar can track moving vehicles at large standoff distances. Unfortunately, trajectories from multiple vehicles can become kinematically ambiguous, resulting in confusion between a target vehicle of interest and other vehicles. We propose the use of high range resolution (HRR) radar profiles and multinomial pattern matching (MPM) for target fingerprinting and track stitching to overcome kinematic ambiguities. Sandia's MPM algorithm is a robust template-based identification algorithm that has been applied successfully to various target recognition problems. MPM utilizes a quantile transformation to map target intensity samples to a small number of grayscale values, or quantiles. The algorithm relies on a statistical characterization of the multinomial distribution of the sample-by-sample intensity values for target profiles. The quantile transformation and statistical characterization procedures are extremely well suited to a robust representation of targets for HRR profiles: they are invariant to sensor calibration, robust to target signature variations, and lend themselves to efficient matching algorithms. In typical HRR tracking applications, target fingerprints must be initiated on the fly from a limited number of HRR profiles. Data may accumulate indefinitely as vehicles are tracked, and their templates must be continually updated without becoming unbounded in size or complexity. To address this need, an incrementally updated version of MPM has been developed. This implementation of MPM incorporates individual HRR profiles as they become available, and fuses data from multiple aspect angles for a given target to aid in track stitching. This paper provides a description of the incrementally updated version of MPM.