Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/87830
Title: Secure dynamic state estimation by decomposing Kalman filter
Authors: Liu, Xinghua
Mo, Yilin
Garone, Emanuele
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Cyber-physical Systems
State Estimation
Issue Date: 2017
Source: Liu, X., Mo, Y., & Garone, E. (2017). Secure dynamic state estimation by decomposing Kalman filter. IFAC-PapersOnLine, 50(1), 7351-7356. doi:10.1016/j.ifacol.2017.08.1491
Series/Report no.: IFAC-PapersOnLine
Abstract: We 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.
URI: https://hdl.handle.net/10356/87830
http://hdl.handle.net/10220/46831
ISSN: 2405-8963
DOI: http://dx.doi.org/10.1016/j.ifacol.2017.08.1491
Rights: © 2017 IFAC (International Federation of Automatic Control). This paper was published in IFAC-PapersOnLine and is made available as an electronic reprint (preprint) with permission of IFAC (International Federation of Automatic Control). The published version is available at: [http://dx.doi.org/10.1016/j.ifacol.2017.08.1491]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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