Distributed Estimation by Partial Sensor Measurements Through Transmission Scheduling for Stochastic Systems

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

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-11-06 DOI:10.1109/TSIPN.2023.3329301

Yun Chen;Yuhang Jin;Jianjun Bai;Mengze Zhu

引用次数: 0

Abstract

This paper is concerned with the partial-sensor-measurements-based (PSMB) distributed estimation problem for a class of stochastic systems (SSs) with randomly occurring nonlinearities, persistent bounded noises and quantization effects. The observations of partial sensor nodes are available to be transmitted to the estimators. In order to enhance the utilization efficiency of limited resources, the Round-Robin protocol is deployed to schedule the data transmissions over communication networks. The sufficient condition is established to guarantee the mean-square exponential ultimate boundedness of the augmented estimation error system (AEES), and then the desired PSMB estimator gains are determined by minimizing the mean-square upper bound of the augmented estimation error vector subject to iterative matrix inequalities. Finally, an illustrative example demonstrates the effectiveness of proposed estimation scheme.

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随机系统传输调度中部分传感器测量的分布估计

研究了一类具有随机非线性、持续有界噪声和量化效应的随机系统的基于部分传感器测量的分布估计问题。部分传感器节点的观测值可以传送给估计器。为了提高有限资源的利用效率，采用轮循协议对通信网络中的数据传输进行调度。首先建立了增广估计误差系统均方指数最终有界的充分条件，然后根据迭代矩阵不等式，通过最小化增广估计误差向量的均方上界来确定期望的PSMB估计器增益。最后，通过实例验证了所提估计方案的有效性。

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.