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Particle filtering with soft state constraints for target tracking

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journal contribution
posted on 02.04.2019, 12:17 by Cunjia Liu, Baibing Li, Wen-Hua Chen
In practice, additional knowledge about the target to be tracked, other than its fundamental dynamics, can often be modelled as a set of soft constraints and utilised in a filtering process to improve the tracking performance. This paper develops a general approach to the modelling of soft inequality constraints, and investigates particle filtering with soft state constraints for target tracking. We develop two particle filtering algorithms with soft inequality constraints, i.e. a sequential-importanceresampling particle filter and an auxiliary sampling mechanism. The latter probabilistically selects the candidate particles from the soft inequality constraints of the state variables so that they are more likely to comply with the soft constraints. The performances of the proposed algorithms are evaluated using Monte Carlo simulations in a target tracking scenario.

Funding

This work was supported in part by the U.K. Engineering and Physical Sciences Research Council (EPSRC) Autonomous and Intelligent Systems programme under the grant number EP/J011525/1 with BAE Systems as the leading industrial partner and in part by the EPSRC grant EP/K014307/2 and the U.K. MOD University Defence Research Collaboration in Signal Processing.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

IEEE Transactions on Aerospace and Electronic Systems

Volume

55

Issue

6

Pages

3492 - 3504

Citation

LIU, C., LI, B. and CHEN, W-H., 2019. Particle filtering with soft state constraints for target tracking. IEEE Transactions on Aerospace and Electronic Systems, doi: 10.1109/TAES.2019.2908292.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

AM (Accepted Manuscript)

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

19/03/2019

Publication date

2019-03-29

Copyright date

2019

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: https://creativecommons.org/licenses/by/3.0/. The accepted version will be replaced by the published version once this is available.

ISSN

0018-9251

eISSN

1557-9603

Language

en