Distributed Generalized Minimum Error Entropy Unscented Kalman Filter Under Hybrid Attacks Without Prior Knowledge

Jiacheng He, Gang Wang, Zhenyu Feng, Shan Zhong, Ping Zhang, Bei Peng

Abstract: This article examines the issue of distributed nonlinear state estimation using generalized minimum error entropy (GMEE) amidst hybrid attacks and heavy-tailed measurement noise, particularly in scenarios, where the characteristics of deception attack and measurement noise are unknown. The variational Bayesian (VB) inference is utilized to deal with the challenge of unknown characteristics of the deception attack and measurement noise. In addition, the GMEE criterion is employed to mitigate the impact of non-Gaussian additive measurement disturbances. Subsequently, a nonlinear distributed state estimation (DSE) approach utilizing covariance intersection over wireless sensor networks (WSNs) constrained by hybrid attacks is developed. In addition, the proposed method’s mean error behavior and consistency are evaluated. Finally, numerical simulations confirm the effectiveness of the proposed algorithm.