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Local design of distributed H ∞ ‐consensus filtering over sensor networks under multiplicative noises and deception attacks
Author(s) -
Han Fei,
Dong Hongli,
Wang Zidong,
Li Gongfa
Publication year - 2019
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.4493
Subject(s) - multiplicative function , filter (signal processing) , deception , control theory (sociology) , filtering problem , node (physics) , filter design , bounded function , matrix (chemical analysis) , mathematics , mathematical optimization , computer science , signal (programming language) , artificial intelligence , engineering , psychology , social psychology , mathematical analysis , materials science , control (management) , structural engineering , composite material , computer vision , programming language
Summary This paper is concerned with the local design of the distributed H ∞ ‐consensus filtering problem for a class of discrete time‐varying systems subject to both multiplicative noises and deception attacks over sensor networks. The target plant and the measuring sensors are disturbed by multiplicative noises with known statistics. The malicious signal involved in deception attacks is constrained by a specific sector‐like bounded condition, which reflects certain tolerable bound on the difference between the attack signal and the true signal. Attention is paid to the design of filter gains for guaranteeing a desirable filtering performance that simultaneously characterizes the filtering accuracy and the consensus requirement. To handle the proposed filtering problem, the supply rate function is firstly chosen for each node and then the dissipation matrix is constructed as a column substochastic matrix based on the stochastic vector dissipation theory. Subsequently, sufficient conditions by means of recursive linear matrix inequalities are presented for each node such that the filtering error and the consensus error satisfy the desirable H ∞ ‐consensus performance index over a finite horizon. Finally, an illustrative simulation is presented to demonstrate the effectiveness of the proposed filter strategy.