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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 e ectiveness of the proposed
algorithms, highlighting the impact of observation uncertainties and deception attacks on estimation
accuracy.Agencia Estatal de InvestigaciónMinisterio de Ciencia e InnovaciónEuropean Regional Development Fund PID2021-124486NB-I00Agencia Estatal de Investigació
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