Unreliable networks with random parameter matrices and time-correlated noises: distributed estimation under deception attacks

Raquel Caballero-Águila; María J. García-Ligero; Aurora Hermoso-Carazo; Josefa Linares-Pérez; Raquel Caballero-Águila; María J. García-Ligero; Aurora Hermoso-Carazo; Josefa Linares-Pérez

doi:10.3934/mbe.2023651

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 8: 14550-14577. doi: 10.3934/mbe.2023651

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Unreliable networks with random parameter matrices and time-correlated noises: distributed estimation under deception attacks

1.
Departamento de Estadística e I.O., Universidad de Jaén, Campus Las Lagunillas, 23071 Jaén, Spain
2.
Departamento de Estadística e I.O., Universidad de Granada, Campus Fuentenueva, 18071 Granada, Spain

Received: 03 May 2023 Revised: 23 June 2023 Accepted: 26 June 2023 Published: 05 July 2023

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 effectiveness of the proposed algorithms, highlighting the impact of observation uncertainties and deception attacks on estimation accuracy.
- Networked systems,
- random parameter matrices,
- time-correlated additive noise,
- random deception attacks,
- distributed estimation
Citation: 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

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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 effectiveness of the proposed algorithms, highlighting the impact of observation uncertainties and deception attacks on estimation accuracy.

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