@misc{10481/84288,
year = {2023},
month = {7},
url = {https://hdl.handle.net/10481/84288},
abstract = {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.},
organization = {Agencia Estatal de Investigación},
organization = {Ministerio de Ciencia e Innovación},
organization = {European Regional Development Fund PID2021-124486NB-I00},
organization = {Agencia Estatal de Investigación},
publisher = {American Institute of Mathematical Sciences},
keywords = {Networked systems},
keywords = {Random parameter matrices},
keywords = {Time-correlated additive noise},
keywords = {Random deception attacks},
keywords = {Distributed estimation},
title = {Unreliable networks with random parameter matrices and time-correlated noises: distributed estimation under deception attacks},
doi = {10.3934/mbe.2023651},
author = {Caballero Águila, Raquel and García-Ligero Ramírez, María Jesús and Hermoso Carazo, Aurora and Linares Pérez, Josefa},
}