文章基本信息

标题：Secure Dynamic State Estimation by Decomposing Kalman Filter
本地全文：下载
作者：Xinghua Liu ; Yilin Mo ; Emanuele Garone 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：7351-7356
DOI：10.1016/j.ifacol.2017.08.1491
语种：English
出版社：Elsevier
摘要：AbstractWe consider the problem of estimating the state of a linear time-invariant Gaussian system in the presence of sparse integrity attacks. The attacker can control p out of m sensors and arbitrarily change the measurements. Under mild assumptions, we can decompose the optimal Kalman estimate as a weighted sum of local state estimates, each of which is derived using only the measurements from a single sensor. Furthermore, we propose a convex optimization based approach, instead of the weighted sum approach, to combine the local estimate into a more secure state estimate. It is shown that our proposed estimator coincides with the Kalman estimator with certain probability when all sensors are benign, and we provide a sufficient condition under which the estimator is stable against the (p, m)-sparse attack when p sensors are compromised. A numerical example is provided to illustrate the performance of the proposed state estimation scheme.
关键词：KeywordsCyber-physical systemsState estimationSecurityOptimization