Paper
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.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yanhua Ruan, Peter K. Willett, and 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); https://doi.org/10.1117/12.352860
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Cited by 6 scholarly publications.
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KEYWORDS
Sensors

Detection and tracking algorithms

Data modeling

Palladium

Data fusion

Radon

Statistical analysis

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