Unreliable networks with random parameter matrices and time-correlated noises: distributed estimation under deception attacks Caballero Águila, Raquel García-Ligero Ramírez, María Jesús Hermoso Carazo, Aurora Linares Pérez, Josefa Networked systems Random parameter matrices Time-correlated additive noise Random deception attacks Distributed estimation 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. 2023-09-06T08:57:27Z 2023-09-06T08:57:27Z 2023-07-05 journal article Raquel Caballero-Águila, María J. García-Ligero, Aurora Hermoso-Carazo, Josefa Linares-Pérez. Unreliable networks with random parameter matrices and time-correlated noises: distributed estimation under deception attacks[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 14550-14577. [doi: 10.3934/mbe.2023651] https://hdl.handle.net/10481/84288 10.3934/mbe.2023651 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional American Institute of Mathematical Sciences