Stackelberg game for secure estimation of linear systems subject to unknown input and smart jamming

• Published in: Journal of the Franklin Institute Vol. 357; no. 5; pp. 3056 - 3074
• Main Authors: Li, Li, Zhang, Huixia, Xia, Yuanqing, Zhu, Cui
• Format: Journal Article
• Language: English
• Published: Elmsford Elsevier Ltd 01.03.2020; Elsevier Science Ltd
• Subjects: Algorithms; Covariance; Estimating techniques; Game theory; Jamming; Linear programming; Linear systems; Sensors; Signal processing; Signal to noise ratio; Smart materials; Smart sensors
• ISSN: 0016-0032; 1879-2693; 0016-0032
• DOI: 10.1016/j.jfranklin.2020.02.011

This paper concentrates on secure estimation for a linear system in the presence of an unknown input and a smart jammer. A smart sensor is used to collect information and conduct a local unbiased minimum-variance estimation algorithm. Estimations of the unknown input and system states are transmitted to a remote estimator only when specific events happen for a reduction of communication cost. The smart sensor and the smart jammer can adaptively adjust their respectively transmission powers to maximize their objectives. A Stackelberg game framework is established to describe an interactive decision process between the smart sensor and the smart jammer. Based on signal-to-noise ratio of communication channel, the remote estimator is designed and convergence on estimation error covariance is given. Furthermore, an example is provided to demonstrate the feasibility of the proposed technique.