Local Decomposition of Kalman Filters and its Application for Secure State Estimation

• 成果类型: Article
• 署名作者: Liu, Xinghua; Mo, Yilin; Garone, Emanuele
• 署名单位: Xi'an University of Technology; Tsinghua University; Tsinghua University; Universite Libre de Bruxelles
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.3044854
• 发表日期: 2021
• 页码: 5037-5044
• 关键词: Cyber-physical systems (CPS); Kalman filter; optimization; security

摘要

This article is concerned with the secure state estimation problem of a linear discrete-time Gaussian system in the presence of sparse integrity attacks. m sensors are deployed to monitor the state and p of them can potentially be compromised by an adversary, whose data can be arbitrarily manipulated by the attacker. We show that the optimal Kalman estimate can be decomposed as a weighted sum of local state estimates. Based on these local estimates, we propose a convex optimization based approach to generate amore secure state estimate. It is proved that our proposed estimator coincides with the Kalman estimator with a certain probability when all sensors are benign. Besides, we establish a sufficient condition under which the proposed estimator is stable against the (p, m)-sparse attack. A numerical example is provided to validate the secure state estimation scheme.