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Distributed Recursive Filtering for Time-Varying Systems with Dynamic Bias over Sensor Networks: Tackling Packet Disorders

Author

Listed:
  • Liu, Dan
  • Wang, Zidong
  • Liu, Yurong
  • Xue, Changfeng
  • Alsaadi, Fuad E.

Abstract

In this paper, a distributed filter is designed for time-varying systems corrupted by dynamic bias and packet disorders over sensor networks, where the plant under consideration includes stochastic bias which is governed by a dynamical equation. Moreover, the transmission delays are present in all sensor-to-filter communication channels, and such delays are described by using random variables that have known probability distributions. We focus on constructing a distributed yet recursive filter under the corruption of dynamic bias plus packet disorders. By means of the inductive method, upper bounds (on attained error covariances of the distributed filter) are first given and later minimized by properly parameterizing filter gains. Subsequently, a sufficient condition is presented to rigorously ensure the mean-square boundedness with respect to attained filtering errors. Finally, an example is given for effectiveness validation.

Suggested Citation

  • Liu, Dan & Wang, Zidong & Liu, Yurong & Xue, Changfeng & Alsaadi, Fuad E., 2023. "Distributed Recursive Filtering for Time-Varying Systems with Dynamic Bias over Sensor Networks: Tackling Packet Disorders," Applied Mathematics and Computation, Elsevier, vol. 440(C).
    • Handle: RePEc:eee:apmaco:v:440:y:2023:i:c:s0096300322007378
    DOI: 10.1016/j.amc.2022.127669
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    References listed on IDEAS

    as
    1. Jingyang Mao & Ying Sun & Xiaojian Yi & Hongjian Liu & Derui Ding, 2021. "Recursive filtering of networked nonlinear systems: a survey," International Journal of Systems Science, Taylor & Francis Journals, vol. 52(6), pages 1110-1128, April.
    2. Lei Liu & Lifeng Ma & Jie Zhang & Yuming Bo, 2021. "Distributed non-fragile set-membership filtering for nonlinear systems under fading channels and bias injection attacks," International Journal of Systems Science, Taylor & Francis Journals, vol. 52(6), pages 1192-1205, April.
    3. Xin-Chun Jia, 2021. "Resource-efficient and secure distributed state estimation over wireless sensor networks: a survey," International Journal of Systems Science, Taylor & Francis Journals, vol. 52(16), pages 3368-3389, December.
    4. Hang Geng & Hongjian Liu & Lifeng Ma & Xiaojian Yi, 2021. "Multi-sensor filtering fusion meets censored measurements under a constrained network environment: advances, challenges and prospects," International Journal of Systems Science, Taylor & Francis Journals, vol. 52(16), pages 3410-3436, December.
    5. Fanrong Qu & Xia Zhao & Xinmeng Wang & Engang Tian, 2022. "Probabilistic-constrained distributed fusion filtering for a class of time-varying systems over sensor networks: a torus-event-triggering mechanism," International Journal of Systems Science, Taylor & Francis Journals, vol. 53(6), pages 1288-1297, April.
    6. Wen Li & Yugang Niu & Zhiru Cao, 2022. "Event-triggered sliding mode control for multi-agent systems subject to channel fading," International Journal of Systems Science, Taylor & Francis Journals, vol. 53(6), pages 1233-1244, April.
    7. Yamei Ju & Xin Tian & Hongjian Liu & Lifeng Ma, 2021. "Fault detection of networked dynamical systems: a survey of trends and techniques," International Journal of Systems Science, Taylor & Francis Journals, vol. 52(16), pages 3390-3409, December.
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