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Multiple model ballistic missile tracking with state-dependent transitions and Gaussian particle filtering

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journal contribution
posted on 30.10.2017 by Miao Yu, Liyun Gong, Hyondong Oh, Wen-Hua Chen, Jonathon Chambers
This paper proposes a new method for tracking the entire trajectory of a ballistic missile from launch to impact on the ground. Multiple state models are used to represent the different ballistic missile dynamics in three flight phases: boost, coast and reentry. In particular, the transition probabilities between state models are represented in a state-dependent way by utilising domain knowledge. Based on this modelling system and radar measurements, a state-dependent interacting multiple model approach based on Gaussian particle filtering is developed to accurately estimate information describing the ballistic missile such as the phase of flight, position, velocity and relevant missile parameters. Comprehensive numerical simulation studies show that the proposed method outperforms the traditional multiple model approaches for ballistic missile tracking.

Funding

This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) Grant number EP/K014307/1 and the 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

Citation

YU, M. ...et al., 2018. Multiple model ballistic missile tracking with state-dependent transitions and Gaussian particle filtering. IEEE Transactions on Aerospace and Electronic Systems, 54(3), pp. 1066-1081.

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 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Acceptance date

16/10/2017

Publication date

2017-11-13

Notes

This is an Open Access Article. It is published by IEEE under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

ISSN

0018-9251

Language

en

Licence

Exports