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Sampled-data distributed state estimation with multiple transmission channels under denial-of-service attacks

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  • Gao, Rui
  • Yang, Guang-Hong

Abstract

This paper is concerned with the problem of sampled-data distributed state estimation for a linear continuous-time system with multiple transmission channels under denial-of-service attacks. To solve this problem, sampled-data distributed observers, which only have access to their respective measurable output variables and the information obtained from their neighbors, are designed, where a decomposition form of the coefficient matrices of the system and a time interval division-based method are proposed to deal with the complex interaction of the sampled-data schemes and the attack schemes among different transmission channels. Different from the previous results, the transmission channels from the sensors to the remote observers do not share the same sampled-data scheme and are allowed to be compromised by different adversaries. An example is provided to validate the effectiveness of the proposed method.

Suggested Citation

  • Gao, Rui & Yang, Guang-Hong, 2022. "Sampled-data distributed state estimation with multiple transmission channels under denial-of-service attacks," Applied Mathematics and Computation, Elsevier, vol. 429(C).
    • Handle: RePEc:eee:apmaco:v:429:y:2022:i:c:s0096300322003034
    DOI: 10.1016/j.amc.2022.127229
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    References listed on IDEAS

    as
    1. Lv, Yuan-Wei & Yang, Guang-Hong, 2022. "An adaptive cubature Kalman filter for nonlinear systems against randomly occurring injection attacks," Applied Mathematics and Computation, Elsevier, vol. 418(C).
    2. Zhang, Zhipeng & Wang, Huimin, 2022. "Resilient decentralized adaptive tracking control for nonlinear interconnected systems with unknown control directions against DoS attacks," Applied Mathematics and Computation, Elsevier, vol. 415(C).
    3. Huang, Xin & Dong, Jiuxiang, 2020. "A Robust Dynamic Compensation Approach for Cyber-Physical Systems Against Multiple Types of Actuator Attacks," Applied Mathematics and Computation, Elsevier, vol. 380(C).
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