Efficient importance sampling function design for sequential Monte Carlo PHD filter

Yoon, J, Kim, D and Yoon, K 2012, 'Efficient importance sampling function design for sequential Monte Carlo PHD filter', Signal Processing, vol. 92, no. 9, pp. 2315-2321.


Document type: Journal Article
Collection: Journal Articles

Title Efficient importance sampling function design for sequential Monte Carlo PHD filter
Author(s) Yoon, J
Kim, D
Yoon, K
Year 2012
Journal name Signal Processing
Volume number 92
Issue number 9
Start page 2315
End page 2321
Total pages 7
Publisher Elsevier
Abstract 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.
Subject Signal Processing
Keyword(s) Importance sampling function
Multi-target filtering
PHD filter
Random finite sets
Unscented information filter
DOI - identifier 10.1016/j.sigpro.2012.01.010
Copyright notice © 2012 Elsevier B.V. All rights reserved.
ISSN 0165-1684
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