@misc{10481/105474,
year = {2025},
month = {3},
url = {https://hdl.handle.net/10481/105474},
abstract = {This study presents recursive algorithms for distributed estimation over a sensor network with a fixed topology, where each sensor node performs estimation using its own data as well as information from neighboring nodes. The algorithms are developed under the assumption that the sensor measurements are quantized and subject to random parameter variations, in addition to time-correlated additive noises. The network is assumed to be exposed to adversarial disruptions, specifically random deception attacks and denial-of-service (DoS) attacks. To address data loss due to DoS attacks, we introduce a compensation strategy that utilizes predicted values to preserve estimation reliability. In the proposed distributed estimation framework, each sensor local processor produces least-squares linear estimators based on both its own and neighboring sensor measurements. These initial estimators are termed early estimators, as those within the neighborhood of each node are subsequently fused in a second stage to yield the final distributed estimators. The algorithms rely on a covariance-based estimation approach that operates without specific structural assumptions about the dynamics of the signal process. A numerical experiment illustrates the applicability and effectiveness of the proposed algorithms and evaluates the effects of adversarial attacks on the estimation accuracy.},
organization = {MICIU/AEI/10.13039/501100011033 and ERDF/EU (grant PID2021-124486NB-I00)},
organization = {National High-end Foreign Experts Recruitment Plan of China (grant G2023012004L)},
publisher = {Elsevier},
keywords = {Networked uncertain systems},
keywords = {Distributed estimation},
keywords = {Quantized measurements},
keywords = {Time-correlated noise},
keywords = {Adversarial attacks},
title = {Distributed estimation for uncertain systems subject to measurement quantization and adversarial attacks},
doi = {10.1016/j.inffus.2025.103044},
author = {Caballero-Águila, Raquel and Hu, Jun and Linares Pérez, Josefa},
}