文献类型：期刊文献

英文题名：Distributed Secure State Estimation Under Stochastic Linear Attacks

作者：Yang, Wen[1];Luo, Weijie[1];Zhang, Xinting[1]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2021

卷号：8

期号：3

起止页码：2036

外文期刊名：IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING

收录：;EI(收录号：20213910960868);WOS:【SCI-EXPANDED(收录号:WOS:000697822000008)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grants 61973123 and 61673177, in part by the projects sponsored by the development fund for Shanghai talents, in part by the Shanghai Natural Science Foundation under !Grant 18ZR1409700, in part by the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017, and in part by the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Detectors; State estimation; Technological innovation; Security; Communication networks; Covariance matrices; Cyber-physical system; False data injection attack; Kullback-Leibler divergence; State estimation

摘要：With the widespread applications of Cyber-Physical Systems, the security problem of distributed state estimation has been exposed, and attracted considerable attentions. In this paper, we consider the issue of distributed secure state estimation under stochastic linear attacks. Inspired by the method applied in anomaly detection, we propose a K-L divergence detector to resist the hostile attacks to guarantee an accurate estimation. For the proposed estimator equipped with the detector, we derive the optimal estimator gain, and establish a sufficient condition to guarantee the convergence of estimation error covariance. Furthermore, we provide a critical threshold selection method for the K-L divergence detector by simplifying the form of K-L divergence under Gaussian distribution. Finally, the effectiveness of the detector is verified by some numerical examples, and the performance comparison with a typical detector is provided.