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An auxiliary particle filtering algorithm with inequality constraints

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journal contribution
posted on 11.11.2016, 14:25 by Baibing Li, Cunjia Liu, Wen-Hua Chen
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.

Funding

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.

History

School

  • Business and Economics

Department

  • Business

Published in

IEEE Transactions on Automatic Control

Volume

62

Issue

9

Pages

4639-4646

Citation

LI, B., LIU, C. and CHEN, W-H., 2017. An auxiliary particle filtering algorithm with inequality constraints. IEEE Transactions on Automatic Control, 62 (9), pp. 4639-4646.

Publisher

© IEEE

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 3.0 Unported (CC BY 3.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/by/3.0/

Acceptance date

28/10/2016

Publication date

2016-11-14

Notes

This is an Open Access Article. It is published by IEEE under the creative Commons Attribution 3.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/3.0/

ISSN

1558-2523

Language

en

Licence

Exports