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  • Distributed H∞-consensus filtering for target state tracking over a wireless filter network with switching topology, channel fading and packet dropouts

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    Embargoed until: 2021-05-30
    Author(s)
    Qu, H
    Yang, F
    Griffith University Author(s)
    Yang, Fuwen
    Year published
    2020
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    Abstract
    This paper is concerned with the problem of distributed H∞-consensus filtering for target state tracking over a wireless filter network. The wireless filter network consists of a large number of filter nodes. The network topology is supposed to be switching or changeable over time. Data communication between filter nodes is subject to some network-induced constraints including packet dropouts and channel fading. Different from some existing results, packet dropouts and channel fading are assumed to occur not only in the measurement process for sensors but also in the transmission process among filter nodes. Within such ...
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    This paper is concerned with the problem of distributed H∞-consensus filtering for target state tracking over a wireless filter network. The wireless filter network consists of a large number of filter nodes. The network topology is supposed to be switching or changeable over time. Data communication between filter nodes is subject to some network-induced constraints including packet dropouts and channel fading. Different from some existing results, packet dropouts and channel fading are assumed to occur not only in the measurement process for sensors but also in the transmission process among filter nodes. Within such settings, a distributed H∞-consensus filtering method is developed to ensure both the estimation accuracy of filters and the robustness of the filtering error system against network-induced constraints. Criteria on designing desired distributed H∞ consensus-based filter are derived in terms of a set of linear matrix inequalities. Numerical examples are given to verify the effectiveness of the proposed target state tracking filtering algorithm.
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    Journal Title
    Neurocomputing
    DOI
    https://doi.org/10.1016/j.neucom.2019.04.081
    Copyright Statement
    © 2019 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Note
    This publication has been entered into Griffith Research Online as an Advanced Online Version
    Subject
    Information and Computing Sciences
    Engineering
    Psychology and Cognitive Sciences
    Publication URI
    http://hdl.handle.net/10072/386733
    Collection
    • Journal articles

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