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With the wide application of network data transmission, network control systems often encounter network attacks in the process of data transmission. In this paper, for a multi-sensor system with network attacks and longitudinal correlation noise, white multiplicity noise is used to describe the state of the system and uncertainty in the observation process. A set of random variables that follow Bernoulli distribution is selected to describe unknown network attacks in the data transmission process. When the observed data are lost, the current observed forecast value is used to compensate. For multi-sensor systems, a global optimal centralized Kalman filter is designed using linear unbiased minimum variance estimation criterion. Simulation results verify the effectiveness and feasibility of the algorithm.

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Centralized Fusion Filter for Multi-sensor Systems with Network Attack and Longitudinal Correlation Noise

  • 会议论文

  • 发表日期：

    2025年

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  • Xiao Liang,
  • Xiaojun Sun,
  • David Tien,
  • Wang Guanran

摘要

With the wide application of network data transmission, network control systems often encounter network attacks in the process of data transmission. In this paper, for a multi-sensor system with network attacks and longitudinal correlation noise, white multiplicity noise is used to describe the state of the system and uncertainty in the observation process. A set of random variables that follow Bernoulli distribution is selected to describe unknown network attacks in the data transmission process. When the observed data are lost, the current observed forecast value is used to compensate. For multi-sensor systems, a global optimal centralized Kalman filter is designed using linear unbiased minimum variance estimation criterion. Simulation results verify the effectiveness and feasibility of the algorithm.