Distributed Secure State Estimation for Cyber-Physical Systems Under False Data Injection Attacks

This paper studies the distributed secure state estimation problem for cyber-physical systems subjected to false data injection attacks. A novel distributed framework is proposed for remote state estimation based on distributed Kalman filtering technology, where the sensors gather neighboring measurement information and send it and local estimation to remote estimators through wireless channels. At the remote end, each estimator is equipped with a threshold-based protector. Compared with the existing results, the proposed protectors are advantageous in terms of saving calculation time. The optimal Kalman gain and the convergence of error covariance are provided under two different attack modes. Finally, a numerical simulation is conducted to verify the effectiveness of state estimation.