Distributed Dimensionality Reduction Fusion Estimation for Stochastic Uncertain Systems With Fading Measurements Subject to Mixed Attacks

In this article, the distributed fusion estimation issue with the dimensionality reduction strategy under DoS attacks and deception attacks is investigated for a class of stochastic uncertain systems with fading measurements. The stochastic uncertainties existed in the system and measurement equatio...

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Vydané v:IEEE transactions on systems, man, and cybernetics. Systems Ročník 52; číslo 11; s. 7053 - 7064
Hlavní autori: Fan, Sha, Yan, Huaicheng, Zhang, Hao, Wang, Yueying, Peng, Yan, Xie, Shaorong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Bandwidth
Covariance matrix
Dimensionality reduction
Distributed dimensionality reduction fusion estimation
DoS attacks and deception attacks
Estimation
Fading
Fading channels
fading measurements
Measurement uncertainty
Multisensor fusion
sequential intersection fusion
Stochastic processes
stochastic uncertainties
Uncertain systems
ISSN:2168-2216, 2168-2232
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Shrnutí:In this article, the distributed fusion estimation issue with the dimensionality reduction strategy under DoS attacks and deception attacks is investigated for a class of stochastic uncertain systems with fading measurements. The stochastic uncertainties existed in the system and measurement equations are represented by state-dependent noises. The fading measurements are depicted by stochastic variables with known statistics. Then, a novel attack and compensation model is proposed to display the randomly occurring behaviors of the DoS attacks and the deception attacks within a unified framework. Furthermore, a distributed multisensor fusion estimation (DMSFE) algorithm is presented. An explicit form of dimensionality reduction is designed against attacks. Stability conditions are derived such that the mean square errors (MSEs) of the proposed DMSFE are bounded. A sequential covariance intersection fusion estimator (SCIFE) is designed to prevent the cross fusion covariance matrices calculating, which owns lower accuracy by smaller computation cost than DMSFE. An illustrative example is provided to show the effectiveness and merits of the proposed algorithm.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2022.3156848