Distributed estimation for multi-sensor networked stochastic uncertain systems with correlated noises under a general stochastic communication protocol

IF 15.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Han Zhou, Shuli Sun
{"title":"Distributed estimation for multi-sensor networked stochastic uncertain systems with correlated noises under a general stochastic communication protocol","authors":"Han Zhou,&nbsp;Shuli Sun","doi":"10.1016/j.inffus.2025.103739","DOIUrl":null,"url":null,"abstract":"<div><div>The distributed state estimation problem is studied for multi-sensor networked stochastic uncertain systems with correlated noises under a stochastic communication protocol (SCP). Random parameter matrices are utilized to describe the stochastic uncertainties within the system model. Given the limited channel bandwidth among sensor nodes, a general SCP is set to randomly select multiple components from the complete state prediction estimate for transmission. A set of random variables is introduced to indicate which combination of state prediction components is selected for transmission at each time step. In the case that the sensor node does not know which combination of state prediction components from each neighboring node is transmitted to it at each time step, a distributed Kalman-like recursive estimator structure that depends on the probability distributions of random variables is developed. Under this estimator structure, an optimal distributed estimation algorithm is presented based on the linear unbiased minimum variance criterion, which necessitates the computation of estimation error cross-covariance matrices between different nodes. To avert the computation of cross-covariance matrices, a suboptimal distributed estimation algorithm is also proposed, where optimal gains are achieved by minimizing the upper bound of estimation error covariance matrix at each node. In addition, the scalar parameters in the upper bound of the covariance matrix are optimized to obtain a minimum upper bound. Stability and steady-state properties of two distributed estimation algorithms are analyzed. Finally, the effectiveness of the presented algorithms is validated through a simulation example.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"127 ","pages":"Article 103739"},"PeriodicalIF":15.5000,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Fusion","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1566253525008012","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

The distributed state estimation problem is studied for multi-sensor networked stochastic uncertain systems with correlated noises under a stochastic communication protocol (SCP). Random parameter matrices are utilized to describe the stochastic uncertainties within the system model. Given the limited channel bandwidth among sensor nodes, a general SCP is set to randomly select multiple components from the complete state prediction estimate for transmission. A set of random variables is introduced to indicate which combination of state prediction components is selected for transmission at each time step. In the case that the sensor node does not know which combination of state prediction components from each neighboring node is transmitted to it at each time step, a distributed Kalman-like recursive estimator structure that depends on the probability distributions of random variables is developed. Under this estimator structure, an optimal distributed estimation algorithm is presented based on the linear unbiased minimum variance criterion, which necessitates the computation of estimation error cross-covariance matrices between different nodes. To avert the computation of cross-covariance matrices, a suboptimal distributed estimation algorithm is also proposed, where optimal gains are achieved by minimizing the upper bound of estimation error covariance matrix at each node. In addition, the scalar parameters in the upper bound of the covariance matrix are optimized to obtain a minimum upper bound. Stability and steady-state properties of two distributed estimation algorithms are analyzed. Finally, the effectiveness of the presented algorithms is validated through a simulation example.
通用随机通信协议下具有相关噪声的多传感器网络随机不确定系统的分布估计
研究了随机通信协议下具有相关噪声的多传感器网络随机不确定系统的分布式状态估计问题。利用随机参数矩阵来描述系统模型中的随机不确定性。在传感器节点间信道带宽有限的情况下,设置一个通用SCP,从完全状态预测估计中随机选择多个组件进行传输。引入一组随机变量来指示在每个时间步选择哪种状态预测分量组合进行传输。针对传感器节点在每个时间步不知道从每个相邻节点传递的状态预测分量组合的情况,提出了一种依赖于随机变量概率分布的分布式类卡尔曼递归估计器结构。在该估计器结构下,提出了一种基于线性无偏最小方差准则的最优分布估计算法,该算法需要计算不同节点间的估计误差交叉协方差矩阵。为了避免交叉协方差矩阵的计算,提出了一种次优分布估计算法,该算法通过最小化每个节点估计误差协方差矩阵的上界来获得最优增益。此外,对协方差矩阵上界的标量参数进行优化,得到最小上界。分析了两种分布式估计算法的稳定性和稳态特性。最后,通过仿真算例验证了算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Information Fusion
Information Fusion 工程技术-计算机:理论方法
CiteScore
33.20
自引率
4.30%
发文量
161
审稿时长
7.9 months
期刊介绍: Information Fusion serves as a central platform for showcasing advancements in multi-sensor, multi-source, multi-process information fusion, fostering collaboration among diverse disciplines driving its progress. It is the leading outlet for sharing research and development in this field, focusing on architectures, algorithms, and applications. Papers dealing with fundamental theoretical analyses as well as those demonstrating their application to real-world problems will be welcome.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信