15 July 1999 Comparison of the PMHT and PDAF tracking algorithms based on their model CRLBs
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Abstract
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.
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Yanhua Ruan, Peter K. Willett, Roy L. Streit, "Comparison of the PMHT and PDAF tracking algorithms based on their model CRLBs", Proc. SPIE 3692, Acquisition, Tracking, and Pointing XIII, (15 July 1999); doi: 10.1117/12.352860; https://doi.org/10.1117/12.352860
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