Caballero-Águila Raquel, García-Ligero María J, Hermoso-Carazo Aurora, Linares-Pérez Josefa
Departamento de Estadística e I.O., Universidad de Jaén, Campus Las Lagunillas, 23071 Jaén, Spain.
Departamento de Estadística e I.O., Universidad de Granada, Campus Fuentenueva, 18071 Granada, Spain.
Math Biosci Eng. 2023 Jul 5;20(8):14550-14577. doi: 10.3934/mbe.2023651.
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.
本文研究了网络系统的分布式滤波和定点平滑问题，考虑了随机参数矩阵、时间相关的加性噪声和随机欺骗攻击。所提出的分布式估计算法包括两个阶段：第一阶段基于本地和相邻节点测量创建中间估计器，而第二阶段使用最小二乘矩阵加权线性组合来组合来自相邻传感器的中间估计器。主要贡献和挑战在于同时考虑各种网络诱导现象，并为信息不完整的系统提供统一框架。这些算法的设计无需特定结构假设，并使用基于协方差的估计技术，该技术不需要了解被估计信号的演化模型。数值实验证明了所提算法的适用性和有效性，突出了观测不确定性和欺骗攻击对估计精度的影响。
