A GMM-Based Secure State Estimation Approach against Dynamic Malicious Adversaries. (29th January 2022)

• Record Type: Journal Article
• Authors: Zhu, Cui; Wang, Zile; Zang, Zeyuan; Li, Yuxuan; Zheng, Huanming
• Other Names: Jin Xue-bo Academic Editor.
• Is Part Of: Journal of sensors. Volume 2022(2022)
• Publication Date: 2022-01-29
• Subjects: Detectors -- Periodicals; 681.205
• DOI: 10.1155/2022/2127102
• Languages: English
• ISSNs: 1687-725X

Abstract

Abstract : We consider the secure state estimation of linear time-invariant Gaussian systems subject to dynamic malicious attacks. An error compensator is proposed to reduce the impact of local error data on state estimation. Based on that, a new estimation algorithm based on the Gaussian mixture model (GMM) aiming at dynamic attacks is proposed, which can cluster the local state estimates autonomously and improve the remote estimation accuracy effectively. The superiority of the proposed algorithm is verified by numerical simulations.