Efficient importance sampling function design for sequential Monte Carlo PHD filter

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In this paper, we propose a novel implementation of the probability hypothesis density (PHD) filter based on the sequential Monte Carlo (SMC) method called SMC-PHD filter. The SMC-PHD filter is analogous to the sequential importance sampling which generates samples using an importance sampling (IS) function. Even though this filter permits general class of IS density function, many previous implementations have simply used the state transition density function. However, this approach leads to a degeneracy problem and renders the filter inefficient. Thus, we propose a novel IS function for the SMC-PHD filter using a combination of an unscented information filter and a gating technique. Further, we use measurement-driven birth target intensities because they are more efficient and accurate than selecting birth targets selected using arbitrary or expected mean target states. The performance of the SMC-PHD filter with the proposed IS function was subsequently evaluated through a simulation and it was shown to outperform the standard SMC-PHD filter and recently proposed auxiliary PHD filter. (C) 2012 Elsevier B.V. All rights reserved.
Publisher
ELSEVIER SCIENCE BV
Issue Date
2012-09
Language
English
Article Type
Article
Keywords

PROBABILISTIC DATA ASSOCIATION; HYPOTHESIS DENSITY FILTER; MULTIPLE-TARGET TRACKING; INFORMATION

Citation

SIGNAL PROCESSING, v.92, no.9, pp.2315 - 2321

ISSN
0165-1684
DOI
10.1016/j.sigpro.2012.01.010
URI
http://hdl.handle.net/10203/240815
Appears in Collection
ME-Journal Papers(저널논문)
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