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19 May 2006 Fixed-lag sequential Monte Carlo data association
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Abstract
The use of multiple scans of data to improve ones ability to improve target tracking performance is widespread in the tracking literature. In this paper, we introduce a novel application of a recent innovation in the SMC literature that uses multiple scans of data to improve the stochastic approximation (and so the data association ability) of a multiple target Sequential Monte Carlo based tracking system. Such an improvement is achieved by resimulating sampled variates over a fixed-lag time window by artificially extending the space of the target distribution. In doing so, the stochastic approximation is improved and so the data association ambiguity is more readily resolved.
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Mark Briers, Arnaud Doucet, Simon R. Maskell, and Paul R. Horridge "Fixed-lag sequential Monte Carlo data association", Proc. SPIE 6236, Signal and Data Processing of Small Targets 2006, 62360S (19 May 2006); https://doi.org/10.1117/12.665684
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Cited by 1 scholarly publication.
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KEYWORDS
Monte Carlo methods

Detection and tracking algorithms

Particles

Manganese

Target detection

Particle filters

Stochastic processes

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