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State dependent multiple model-based particle filtering for ballistic missile tracking in a low-observable environment

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journal contribution
posted on 31.05.2017 by Miao Yu, Wen-Hua Chen, Jonathon Chambers
This paper proposes a new method for tracking the whole trajectory of a ballistic missile (BM), in a low-observable environment with ‘imperfect’ sensor measurement incorporating both miss detection and false alarms. A hybrid system with state dependent transition probabilities is proposed where multiple state models represent the ballistic missile movement during different phases; and domain knowledge is exploited to model the transition probabilities between different flight phases in a state-dependent way. The random finite set (RFS) is adopted to model radar sensor measurements which include both miss detection and false alarms. Based on the proposed hybrid modeling system and the RFS represented sensor measurements, a state dependent interacting multiple model particle filtering method integrated with a generalized measurement likelihood function is developed for the BM tracking. Comprehensive simulation studies show that the proposed method outperforms the traditional ones for the BM tracking, with more accurate estimations of flight mode probabilities, positions and velocities.

Funding

This work was supported by the UK 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

Aerospace Science and Technology

Volume

67

Pages

144 - 154

Citation

YU, M., CHEN, W.-H. and CHAMBERS, J., 2017. State dependent multiple model-based particle filtering for ballistic missile tracking in a low-observable environment. Aerospace Science and Technology, 67, pp.144-154

Publisher

© Elsevier

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

15/03/2017

Publication date

2017

Notes

This paper was published in the journal Aerospace Science and Technology and the definitive published version is available at http://dx.doi.org/10.1016/j.ast.2017.03.028.

ISSN

1270-9638

Language

en

Exports