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Title: An auxiliary particle filtering algorithm with inequality constraints
Authors: Li, Baibing
Liu, Cunjia
Chen, Wen-Hua
Keywords: Auxiliary particle filter
Bayesian inference
Inequality constraints
Sequential Monte Carlo
State space models
Issue Date: 2017
Publisher: © IEEE
Citation: LI, B., LIU, C. and CHEN, W-H., 2017. An auxiliary particle filtering algorithm with inequality constraints. IEEE Transactions on Automatic Control, doi: 10.1109/TAC.2016.2624698
Abstract: For nonlinear non-Gaussian stochastic dynamic systems with inequality state constraints, this paper presents an efficient particle filtering algorithm, constrained auxiliary particle filtering algorithm. To deal with the state constraints, the proposed algorithm probabilistically selects particles such that those particles far away from the feasible area are less likely to propagate into the next time step. To improve on the sampling efficiency in the presence of inequality constraints, it uses a highly effective method to perform a series of constrained optimization so that the importance distributions are constructed efficiently based on the state constraints. The caused approximation errors are corrected using the importance sampling method. This ensures that the obtained particles constitute a representative sample of the true posterior distribution. A simulation study on vehicle tracking is used to illustrate the proposed approach.
Description: This paper is published as Open Access by IEEE.
Sponsor: This work was supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC) Autonomous and Intelligent Systems programme (grant number EP/J011525/1) with BAE Systems as the leading industrial partner.
Version: Accepted for publication
DOI: 10.1109/TAC.2016.2624698
URI: https://dspace.lboro.ac.uk/2134/23142
Publisher Link: http://dx.doi.org/10.1109/TAC.2016.2624698
ISSN: 1558-2523
Appears in Collections:Published Articles (Aeronautical and Automotive Engineering)
Published Articles (Business School)

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