{"title":"Variance-Constrained Distributed Filtering Under Limited Bit Rates for Time-Varying Systems","authors":"Yinghao Hong;Yun Chen;Xueyang Meng;Yunfei Guo","doi":"10.1109/TSIPN.2025.3600831","DOIUrl":null,"url":null,"abstract":"This article concentrates on the variance-constrained distributed filtering problem with the constraint of limited bit rates and imperfect measurements for nonlinear time-varying systems. The measurement outputs undergo the phenomena of sensor saturations and nonlinearities occurring in a random way. An encoding-decoding mechanism (EDM) is implemented to regulate the transmission procedures over shared communication network. The main purpose of this article is to formulate a suitable distributed filtering algorithm to enable the fulfillment of both stochastic <inline-formula><tex-math>$H_{\\infty }$</tex-math></inline-formula> performance and variance constraint for the resultant filtering error system over a finite horizon. The sufficient conditions are initially established to satisfy the prescribed performance constraints, following which the proper filter parameters are derived by means of the solutions to a sequence of iterative matrix inequalities. Furthermore, based on the variance constraint analysis for filtering error, the genetic algorithm (GA) is utilized to optimize the bit rate allocation among every node by minimizing the value of triggered decoding error. Finally, the validity of the proposed distributed filtering scheme is testified by a numerical example.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"1100-1111"},"PeriodicalIF":3.0000,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Signal and Information Processing over Networks","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/11134788/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
This article concentrates on the variance-constrained distributed filtering problem with the constraint of limited bit rates and imperfect measurements for nonlinear time-varying systems. The measurement outputs undergo the phenomena of sensor saturations and nonlinearities occurring in a random way. An encoding-decoding mechanism (EDM) is implemented to regulate the transmission procedures over shared communication network. The main purpose of this article is to formulate a suitable distributed filtering algorithm to enable the fulfillment of both stochastic $H_{\infty }$ performance and variance constraint for the resultant filtering error system over a finite horizon. The sufficient conditions are initially established to satisfy the prescribed performance constraints, following which the proper filter parameters are derived by means of the solutions to a sequence of iterative matrix inequalities. Furthermore, based on the variance constraint analysis for filtering error, the genetic algorithm (GA) is utilized to optimize the bit rate allocation among every node by minimizing the value of triggered decoding error. Finally, the validity of the proposed distributed filtering scheme is testified by a numerical example.
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.