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.
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David J. Salmond, David J. Salmond, Nicholas O. Everett, Nicholas O. Everett, } "Bayesian approach to avoiding track seduction", Proc. SPIE 4728, Signal and Data Processing of Small Targets 2002, (7 August 2002); doi: 10.1117/12.478523; https://doi.org/10.1117/12.478523
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