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A particle filter using SVD based sampling Kalman filter to obtain the proposal distribution

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3 Author(s)
Bin Liu ; Inst. of Acoust., Chinese Acad. of Sci., Beijing ; Xiao-chuan Ma ; Chao-huan Hou

In this paper, we propose a novel particle filter (PF), which uses a bank of singular-value-decomposition based sampling Kalman filters (SVDSKF) to obtain the importance proposal distribution. This proposal has two properties. Firstly, it allows the particle filter to incorporate the latest observations into a prior updating routine and, secondly it inherits advantage of having good numerical stability from the singular-value-decomposition (SVD). The convergence results of the numerical simulations we made confirm that the proposed PF method outperforms the standard bootstrap PF as well as other local linearization based PFs.

Published in:

Cybernetics and Intelligent Systems, 2008 IEEE Conference on

Date of Conference:

21-24 Sept. 2008

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