{"title":"Cramer-Rao Bound Analysis of Localization Using Signal Strength Difference as Location Fingerprint","authors":"A. Hossain, Wee-Seng Soh","doi":"10.1109/INFCOM.2010.5462020","DOIUrl":null,"url":null,"abstract":"In this paper, we analyze the Cramer-Rao Lower Bound (CRLB) of localization using Signal Strength Difference (SSD) as location fingerprint. This analysis has a dual purpose. Firstly, the properties of the bound on localization error may help to design efficient localization algorithm. For example, utilizing one of the properties, we propose a way to define weights for a weighted K-Nearest Neighbor (K-NN) scheme which is shown to perform better than the K-NN algorithm. Secondly, it provides suggestions for a positioning system design by revealing error trends associated with the system deployment. In both cases, detailed analysis as well as experimental results are presented in order to support our claims.","PeriodicalId":259639,"journal":{"name":"2010 Proceedings IEEE INFOCOM","volume":"366 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"88","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Proceedings IEEE INFOCOM","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INFCOM.2010.5462020","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 88
Abstract
In this paper, we analyze the Cramer-Rao Lower Bound (CRLB) of localization using Signal Strength Difference (SSD) as location fingerprint. This analysis has a dual purpose. Firstly, the properties of the bound on localization error may help to design efficient localization algorithm. For example, utilizing one of the properties, we propose a way to define weights for a weighted K-Nearest Neighbor (K-NN) scheme which is shown to perform better than the K-NN algorithm. Secondly, it provides suggestions for a positioning system design by revealing error trends associated with the system deployment. In both cases, detailed analysis as well as experimental results are presented in order to support our claims.