Paper
7 August 2002 Bayesian approach to avoiding track seduction
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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.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
David J. Salmond and Nicholas O. Everett "Bayesian approach to avoiding track seduction", Proc. SPIE 4728, Signal and Data Processing of Small Targets 2002, (7 August 2002); https://doi.org/10.1117/12.478523
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Cited by 1 scholarly publication.
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KEYWORDS
Digital filtering

Monte Carlo methods

Sensors

Detection and tracking algorithms

Data modeling

Neodymium

Target detection

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