Comparison of the PMHT and PDAF tracking algorithms based on their model CRLBs
Yanhua Ruan, Peter Willett, Roy L. Streit · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1999
The PMHT is a very nice tracking algorithm for a number of implementational reasons. However, it relies on a modification on the usual data association assumption, specifically that the event that a target can generate more than one measurement in a given scan is made feasible. In this paper we examine the ramifications of this from the point of view of theoretical estimation accuracy - the Cramer-Rao lower bound. We find that the CRLB behavior for the PMHT is much like that for the PDAF: there is a scalar 'information reduction factor' (IRF) relating the loss of accuracy from measurement-origin-uncertainty. This IRF ix explored in a number of ways, and in particular it is found that the IRF for the PMHT is significantly degraded relative to that for the standard measurement model when clutter is heavy. Other topics include the effect of 'homothetic' measurements; data fusion; and non-Gaussian measurement.