State Estimation for Nonlinear Cyber-Physical Systems With Sensor Failures and Token Bucket Protocol Under False Data Injection Attacks
针对传感器故障和虚假数据注入攻击下的非线性信息物理系统，提出一种结合令牌桶协议的递归状态估计算法，推导误差协方差上界并优化估计器增益，仿真验证有效性。
This article is concerned with the recursive state estimation issue for a class of nonlinear cyber-physical systems (CPSs) with token bucket protocols (TBPs) subject to sensor failures and false data injection (FDI) attacks. In the system under consideration, measurement signals are transmitted to the remote estimator only when there are sufficient tokens in the bucket to meet the token consumption. During network transmissions, the signals are exposed to FDI attacks, which occur randomly and follow a Bernoulli distribution. The primary objective is to develop a state estimation algorithm that can handle the TBP, sensor failures, and FDI attacks simultaneously. Initially, the upper bound of the estimation error covariance is derived using an intensive stochastic technique and the induction approach. Subsequently, the desired estimator gains are recursively computed to minimize this upper bound. Finally, an example is presented to demonstrate the effectiveness of the proposed estimation scheme.