Event-based distributed state estimation for linear systems under unknown input and false data injection attack

•For each node, an event-triggered transmission scheme is designed to reduce the impact of attack on estimation performance.•A novel event-based distributed state estimator is derived, where filter gains are obtained by solving two Riccati-like difference equations.•Sufficient conditions are establi...

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Published inSignal processing Vol. 170; p. 107423
Main Authors Li, Li, Yang, Huan, Xia, Yuanqing, Yang, Hongjiu
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.05.2020
Subjects
Online AccessGet full text
ISSN0165-1684
1872-7557
DOI10.1016/j.sigpro.2019.107423

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Abstract •For each node, an event-triggered transmission scheme is designed to reduce the impact of attack on estimation performance.•A novel event-based distributed state estimator is derived, where filter gains are obtained by solving two Riccati-like difference equations.•Sufficient conditions are established to ensure the asymptotic boundedness of estimation error covariance. This paper investigates an event-based distributed state estimation problem for a linear system subject to unknown input and false data injection attack. The unknown input is treated as a process with a non-informative prior. An event-triggered transmission scheme is designed to reduce exchange of unreliable state estimate information between sensor nodes. In order to defend against false data injection attack, a novel event-based distributed state estimator is proposed by using state estimate information that comes from individual node and neighboring nodes, where filter gains are derived by minimizing an upper bound of estimation error covariance. Then sufficient conditions are established to ensure asymptotic boundedness of the designed estimation error covariance. Finally, effectiveness of the proposed technique is demonstrated by a numerical example.
AbstractList •For each node, an event-triggered transmission scheme is designed to reduce the impact of attack on estimation performance.•A novel event-based distributed state estimator is derived, where filter gains are obtained by solving two Riccati-like difference equations.•Sufficient conditions are established to ensure the asymptotic boundedness of estimation error covariance. This paper investigates an event-based distributed state estimation problem for a linear system subject to unknown input and false data injection attack. The unknown input is treated as a process with a non-informative prior. An event-triggered transmission scheme is designed to reduce exchange of unreliable state estimate information between sensor nodes. In order to defend against false data injection attack, a novel event-based distributed state estimator is proposed by using state estimate information that comes from individual node and neighboring nodes, where filter gains are derived by minimizing an upper bound of estimation error covariance. Then sufficient conditions are established to ensure asymptotic boundedness of the designed estimation error covariance. Finally, effectiveness of the proposed technique is demonstrated by a numerical example.
ArticleNumber 107423
Author Xia, Yuanqing
Yang, Hongjiu
Li, Li
Yang, Huan
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Keywords False data injection attack
Distributed state estimation
Unknown input
Event-triggered transmission scheme
Language English
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Snippet •For each node, an event-triggered transmission scheme is designed to reduce the impact of attack on estimation performance.•A novel event-based distributed...
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SubjectTerms Distributed state estimation
Event-triggered transmission scheme
False data injection attack
Unknown input
Title Event-based distributed state estimation for linear systems under unknown input and false data injection attack
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