Caballero-Águila, RaquelGarcía-Ligero, María J.Hermoso-Carazo, AuroraLinares-Pérez, Josefa2025-01-292025-01-292023-07-05Caballero-Águila, R., García-Ligero, M. J., Hermoso-Carazo, A., Linares-Pérez, J. (2023). Unreliable networks with random parameter matrices and time-correlated noises: distributed estimation under deception attacks. Math. Biosci. Eng., 20(8), 14550–14577.1551-0018http://dx.doi.org/10.3934/mbe.2023651https://hdl.handle.net/10953/4509This 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.engAtribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/Networked systemsRandom parameter matricesTime-correlated additive noiseRandom deception attacksDistributed estimationinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccess