Distributed maximum correntropy unscented Kalman filter under hybrid attacks in non-Gaussian environment

This paper addresses a distributed nonlinear filtering issue based on maximum correntropy for dealing with randomly occurring hybrid cyber-attacks in non-Gaussian environment. The types of cyber-attacks include denial of service attacks and deception attacks. First, a modified distributed unscented Kalman filter is proposed, using Cauchy kernel-based maximum correntropy criterion instead of the traditional mean square error criterion, against cyber-attacks and non-Gaussian noise. Then based on fixed-point iterative rules and information fusion strategy, the update equations of state estimates and covariance matrices of the proposed filter are obtained. Furthermore, the sufficient condition guaranteeing its convergence and computational complexity are also given. Finally, an illustrative example is presented to verify the effectiveness of the proposed filter under hybrid cyber-attacks in non-Gaussian environment.