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Proceedings Paper

Bayesian approach to avoiding track seduction
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Paper Abstract

The problem of maintaining track on a primary target in the presence spurious objects is addressed. Recursive and batch filtering approaches are developed. For the recursive approach, a Bayesian track splitting filter is derived which spawns candidate tracks if there is a possibility of measurement misassociation. The filter evaluates the probability of each candidate track being associated with the primary target. The batch filter is a Markov-chain Monte Carlo (MCMC) algorithm which fits the observed data sequence to models of target dynamics and measurement-track association. Simulation results are presented.

Paper Details

Date Published: 7 August 2002
PDF: 12 pages
Proc. SPIE 4728, Signal and Data Processing of Small Targets 2002, (7 August 2002); doi: 10.1117/12.478523
Show Author Affiliations
David J. Salmond, QinetiQ (United Kingdom)
Nicholas O. Everett, QinetiQ and Univ. of Oxford (United Kingdom)


Published in SPIE Proceedings Vol. 4728:
Signal and Data Processing of Small Targets 2002
Oliver E. Drummond, Editor(s)

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