Probability-Guaranteed Distributed Estimation for Two-Dimensional Systems Under Stochastic Access Protocol

IF 3 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-11 DOI:10.1109/TSIPN.2024.3375596

Meiyu Li;Jinling Liang

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Abstract

This paper studies the probability-guaranteed distributed estimation problem for a kind of two-dimensional shift-varying sensor networks under the stochastic access protocol (SAP). The considered system is affected by unknown-but-bounded perturbations and sector bounded nonlinearity. The communication architecture of a multi-node network is expressed by a digraph. Due to the limited communication channel, each moment allows only one adjacent node to send its measurement data and schedules the signal transmission of the addressing system using the SAP, characterized by a series of independent random variables. For each smart sensor, we designed a distributed estimator based on the network topology as well as the SAP and derived sufficient conditions to ascertain the probability of the estimation error located in the desired ellipsoid being not less than the predetermined value. Collection of these probability ellipsoids acquired at each position is then minimized by solving a set of convex optimization problems in the meaning of matrix trace. Finally, efficiency of the estimator design strategy proposed is demonstrated using a numerical example.

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随机访问协议下二维系统的概率保证分布式估计

本文研究了随机接入协议（SAP）下一种二维位移变化传感器网络的概率保证分布式估计问题。所考虑的系统受到未知但有界的扰动和扇区有界非线性的影响。多节点网络的通信架构用数字图表示。由于通信信道有限，每个时刻只允许一个相邻节点发送测量数据，并使用 SAP 调度寻址系统的信号传输，SAP 由一系列独立随机变量组成。我们为每个智能传感器设计了基于网络拓扑结构和 SAP 的分布式估算器，并推导出充分条件，以确定位于所需椭球体中的估算误差概率不小于预定值。然后，通过求解一系列矩阵跟踪意义上的凸优化问题，最小化在每个位置获取的这些概率椭圆的集合。最后，通过一个数值示例展示了所提出的估算器设计策略的效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications

CiteScore

5.80

自引率

12.50%

发文量

期刊介绍： The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.