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• W4383269898 abstract "<abstract><p>This paper examines the distributed filtering and fixed-point smoothing problems for networked systems, considering random parameter matrices, time-correlated additive noises and random deception attacks. The proposed distributed estimation algorithms consist of two stages: the first stage creates intermediate estimators based on local and adjacent node measurements, while the second stage combines the intermediate estimators from neighboring sensors using least-squares matrix-weighted linear combinations. The major contributions and challenges lie in simultaneously considering various network-induced phenomena and providing a unified framework for systems with incomplete information. The algorithms are designed without specific structure assumptions and use a covariance-based estimation technique, which does not require knowledge of the evolution model of the signal being estimated. A numerical experiment demonstrates the applicability and effectiveness of the proposed algorithms, highlighting the impact of observation uncertainties and deception attacks on estimation accuracy.</p></abstract>" @default.
• W4383269898 created "2023-07-06" @default.
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• W4383269898 date "2023-01-01" @default.
• W4383269898 modified "2023-10-14" @default.
• W4383269898 title "Unreliable networks with random parameter matrices and time-correlated noises: distributed estimation under deception attacks" @default.
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• W4383269898 doi "https://doi.org/10.3934/mbe.2023651" @default.
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