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Particle filtering with soft state constraints for target tracking
journal contribution
posted on 2019-04-02, 12:17 authored by Cunjia LiuCunjia Liu, Baibing LiBaibing Li, Wen-Hua ChenWen-Hua ChenIn 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 SystemsVolume
55Issue
6Pages
3492 - 3504Citation
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
2019-03-19Publication date
2019-03-29Copyright date
2019Notes
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
0018-9251eISSN
1557-9603Publisher version
Language
- en