{"title":"An Impulsive Noise-Resistant Target Localization Approach With Unknown Model Parameter Learning","authors":"Qingli Yan;Zhe Luo;Hui-Ming Wang;Bin Wang;Cong Gao","doi":"10.1109/JIOT.2024.3494870","DOIUrl":null,"url":null,"abstract":"Received signal strength (RSS)-based localization techniques have gained much attention in location-based services (LBSs). However, the coexistence of unknown path loss exponent (PLE), uncertain sensor positions, and impulsive noise poses serious challenges to localization accuracy. To address the problem, we first model the impulsive noise as a Mixture of Gaussian (MoG) distribution with unknown parameters. Thus, the noise model and the channel model can be refined using the observed data under the variational Bayesian inference (VBI) framework, which is defined as the model refinement learning. We then propose a corresponding online target localization procedure with the refined noise distribution, PLE and sensor positions. The Bayesian Cramer-Rao bound (BCRB) is finally derived in terms of all unknown parameters. Simulation results together with real experiment demonstrate that the proposed VBI algorithm can effectively learn the true noise distribution, and the developed localization method exhibits robust localization performance in various scenarios.","PeriodicalId":54347,"journal":{"name":"IEEE Internet of Things Journal","volume":"12 6","pages":"7421-7433"},"PeriodicalIF":8.2000,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Internet of Things Journal","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10752567/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Received signal strength (RSS)-based localization techniques have gained much attention in location-based services (LBSs). However, the coexistence of unknown path loss exponent (PLE), uncertain sensor positions, and impulsive noise poses serious challenges to localization accuracy. To address the problem, we first model the impulsive noise as a Mixture of Gaussian (MoG) distribution with unknown parameters. Thus, the noise model and the channel model can be refined using the observed data under the variational Bayesian inference (VBI) framework, which is defined as the model refinement learning. We then propose a corresponding online target localization procedure with the refined noise distribution, PLE and sensor positions. The Bayesian Cramer-Rao bound (BCRB) is finally derived in terms of all unknown parameters. Simulation results together with real experiment demonstrate that the proposed VBI algorithm can effectively learn the true noise distribution, and the developed localization method exhibits robust localization performance in various scenarios.
期刊介绍:
The EEE Internet of Things (IoT) Journal publishes articles and review articles covering various aspects of IoT, including IoT system architecture, IoT enabling technologies, IoT communication and networking protocols such as network coding, and IoT services and applications. Topics encompass IoT's impacts on sensor technologies, big data management, and future internet design for applications like smart cities and smart homes. Fields of interest include IoT architecture such as things-centric, data-centric, service-oriented IoT architecture; IoT enabling technologies and systematic integration such as sensor technologies, big sensor data management, and future Internet design for IoT; IoT services, applications, and test-beds such as IoT service middleware, IoT application programming interface (API), IoT application design, and IoT trials/experiments; IoT standardization activities and technology development in different standard development organizations (SDO) such as IEEE, IETF, ITU, 3GPP, ETSI, etc.