MLPDA and MLPMHT Applied to Some MSTWG Data

Abstract

About 15 years ago the maximum likelihood probabilistic data association (MLPDA) tracking architecture was proposed; it was found to be a very effective (perhaps the only) way to track very low-observable (VLO) contacts. The MLPDA is based on maximizing statistical likelihood according to a precise model in which there is no process noise: the target's trajectory is deterministic given the parameters (usually initial position and velocity) over which maximization is done. The MLPDA maximizes the likelihood based upon an assumption that each target can generate at most one contact per scan of data. The PMHT (probabilistic multi-hypothesis tracker) provides an alternative perspective: each contact may be as independent and a-priori equally-equipped to be target-generated. Our results indicate that the MLPMHT is the better tracker in multi-static data. Not only is the concept of a of data less relevant for it than the MLPDA (to frame data over a long ping is suspect) since each measurement is treated independently, and not only is optimization simpler since an EM technique can replace direct optimization after the grid search; but it appears that it both works more robustly and is able to avoid contact starvation during periods of poor SNR. A further advantage of the MLPMHT is that optimal data association with multiple targets is easily incorporated, whereas in the MLPDA it is approximated by excision of measurements that are taken by previously-discovered targets.