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Title: Multiple model ballistic missile tracking with state-dependent transitions and Gaussian particle filtering
Authors: Yu, Miao
Gong, Liyun
Oh, Hyondong
Chen, Wen-Hua
Chambers, Jonathon
Keywords: Ballistic missile tracking
Multiple state models
State-dependent transition probabilities
Bayesian inference
Gaussian particle filter
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
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.
Abstract: 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.
Description: 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/
Sponsor: 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
Version: Accepted for publication
DOI: 10.1109/TAES.2017.2773258
URI: https://dspace.lboro.ac.uk/2134/27191
Publisher Link: https://doi.org/10.1109/TAES.2017.2773258
ISSN: 0018-9251
Appears in Collections:Published Articles (Aeronautical and Automotive Engineering)

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