Unreliable networks with random parameter matrices and time-correlated noises: distributed estimation under deception attacks
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2023-07-05
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AIMSPRESS - American Institute of Mathematical Sciences
Resumen
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.
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Palabras clave
Networked systems, Random parameter matrices, Time-correlated additive noise, Random deception attacks, Distributed estimation
Citación
Caballero-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.