Maximum correntropy criterion-based Kalman filter for replay attack in non-Gaussian noises

The Kalman filter (KF) has been widely recognized as a fundamental tool for state estimation in linear systems. However, its performance can be significantly degraded when the measurement is subjected to replay attacks, particularly in non-Gaussian noise environments. To tackle this challenge, this letter presents a new robust KF, called replay attack maximum correntropy Kalman filter (RAMCKF). First, a replay attack measurement model (RAMM) is proposed, generating a novel state space model under replay attacks. Moreover, the maximum correntropy criterion (MCC) is used to construct the cost function that is effective to combat non-Gaussian noises. Then, the RAMCKF is proposed by the optimal estimation theory. Finally, simulation results demonstrate the robustness and adaptability of the proposed algorithm in replay attack scenarios and non-Gaussian disturbances.