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
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.