{"title":"Random sampling algorithm in RFID indoor location system","authors":"Bao Xu, Wang Gang","doi":"10.1109/DELTA.2006.73","DOIUrl":null,"url":null,"abstract":"In this paper, low cost radio frequency identification (RFID) indoor location scheme is proposed by deploying RFID tags and implementing a new localization algorithm to make person holding RFID reader know where he is in real time. In this algorithm, the person's state space is represented by maintaining a set of random samples. And a localization method that can represent arbitrary distributions is proposed by using a sampling-based representation. Comparison between proposed algorithm and least square (LS) algorithm in TOA indicated that the positioning errors of the proposed algorithm are lower than LS under the nonline-of sight (NLOS) scenarios. For the case that LS is not available when less than three tags deployed, however, the proposed algorithm can keep track of person.","PeriodicalId":439448,"journal":{"name":"Third IEEE International Workshop on Electronic Design, Test and Applications (DELTA'06)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"53","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Third IEEE International Workshop on Electronic Design, Test and Applications (DELTA'06)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DELTA.2006.73","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 53
Abstract
In this paper, low cost radio frequency identification (RFID) indoor location scheme is proposed by deploying RFID tags and implementing a new localization algorithm to make person holding RFID reader know where he is in real time. In this algorithm, the person's state space is represented by maintaining a set of random samples. And a localization method that can represent arbitrary distributions is proposed by using a sampling-based representation. Comparison between proposed algorithm and least square (LS) algorithm in TOA indicated that the positioning errors of the proposed algorithm are lower than LS under the nonline-of sight (NLOS) scenarios. For the case that LS is not available when less than three tags deployed, however, the proposed algorithm can keep track of person.
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