Worst-Case Stealthy Attacks on Stochastic Event-Based State Estimation

• 成果类型: Article
• 署名作者: Shang, Jun; Yu, Hao; Chen, Tongwen
• 署名单位: University of Alberta
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2021.3071948
• 发表日期: 2022
• 页码: 2052-2059
• 关键词: State estimation; Technological innovation; Kalman filters; probability distribution; Covariance matrices; Systems architecture; estimation error; Communication rates; event-based sensor schedule; remote state estimation; stealthy attacks

摘要

This article considers the worst-case stealthy attack strategies on stochastic event-based state estimation. Smart sensors equipped with local event-triggered Kalman filters are used to transmit innovations. Contrary to classic Kalman filters, the transmitted innovation screened by the stochastic decision rule does not follow a Gaussian distribution. A type of distribution called complete Gaussian crater is defined and analyzed, which is essential for designing stealthy attacks. The evolution of the estimation error covariance under attacks is obtained. Stealthy attacks that yield the greatest estimation errors under constraints on transmission rates and probability distributions are obtained and analyzed. The system performance degradation caused by different attacks is evaluated via simulations.