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Unscented Kalman filtering for nonlinear systems with sensor saturation and randomly occurring false data injection attacks
Author(s) -
Lu Jiyong,
Wang Weizhen,
Li Li,
Guo Yanping
Publication year - 2021
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.2262
Subject(s) - bernoulli distribution , kalman filter , control theory (sociology) , nonlinear system , unscented transform , covariance , bernoulli's principle , mathematics , extended kalman filter , saturation (graph theory) , bounded function , random variable , invariant extended kalman filter , computer science , algorithm , engineering , statistics , artificial intelligence , mathematical analysis , physics , control (management) , quantum mechanics , aerospace engineering , combinatorics
In this paper, an unscented Kalman filtering problem is studied for a nonlinear system with sensor saturation and randomly occurring false data injection attacks. A random variable obeying the Bernoulli distribution is employed to characterize the phenomena of the randomly occurring false data injection attacks. The aim of this paper is to design a modified unscented Kalman filter by minimizing an upper bound of filtering error covariance. Furthermore, a sufficient condition is provided to ensure an exponentially bounded filtering error in the mean square sense. Numerical simulations are presented to illustrate the validity of the proposed filter.