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Event-Triggered Extended Kalman Filtering Analysis for Networked Systems

Author

Listed:
  • Huijuan Zhao

    (School of Mathematics, East China University of Science and Technology, Shanghai 200237, China)

  • Jiapeng Xu

    (Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON N9B 3P4, Canada)

  • Fangfei Li

    (School of Mathematics, East China University of Science and Technology, Shanghai 200237, China
    Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China)

Abstract

In this paper, the filtering problem of nonlinear networked systems with event-triggered data transmission tasks is studied. To reduce the transmission of excessive measurement data in the bandwidth-limited network, a data transmission mechanism with event trigger is introduced to analyze the error behavior of the extended Kalman filter. We prove that the real estimation error and error covariance matrices can be determined by restricting the initial conditions appropriately. Finally, the effectiveness of the filtering algorithm is verified by simulation.

Suggested Citation

  • Huijuan Zhao & Jiapeng Xu & Fangfei Li, 2022. "Event-Triggered Extended Kalman Filtering Analysis for Networked Systems," Mathematics, MDPI, vol. 10(6), pages 1-12, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:927-:d:770704
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    References listed on IDEAS

    as
    1. María Jesús García-Ligero & Aurora Hermoso-Carazo & Josefa Linares-Pérez, 2020. "Distributed Fusion Estimation with Sensor Gain Degradation and Markovian Delays," Mathematics, MDPI, vol. 8(11), pages 1-19, November.
    Full references (including those not matched with items on IDEAS)

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