M. Agatonovic, E. Di Giampaolo, P. Tognolatti, B. Milovanovic
{"title":"Artificial Neural Networks for ranging of passive UHF RFID tags","authors":"M. Agatonovic, E. Di Giampaolo, P. Tognolatti, B. Milovanovic","doi":"10.1109/TELSKS.2013.6704428","DOIUrl":null,"url":null,"abstract":"Ranging of passive Ultra High Frequency (UHF) Radio Frequency Identification (RFID) tags in indoor environments is a topical issue nowadays. Due to complexity of such an environment, there is no effective solution to this problem. In this paper we investigate application of Artificial Neural Networks (ANNs) in indoor localization of passive UHF RFID tags. Namely, we estimate distance between a reader antenna and a couple of tags attached to an item, using nonlinear mapping that ANNs perform between measured values of the Received Signal Strength Indicator (RSSI), turn on power and phase on the one hand, and the distance on the other. The proposed ANN model calculates distance with an average error of 7.31 cm.","PeriodicalId":144044,"journal":{"name":"2013 11th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Services (TELSIKS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 11th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Services (TELSIKS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TELSKS.2013.6704428","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Ranging of passive Ultra High Frequency (UHF) Radio Frequency Identification (RFID) tags in indoor environments is a topical issue nowadays. Due to complexity of such an environment, there is no effective solution to this problem. In this paper we investigate application of Artificial Neural Networks (ANNs) in indoor localization of passive UHF RFID tags. Namely, we estimate distance between a reader antenna and a couple of tags attached to an item, using nonlinear mapping that ANNs perform between measured values of the Received Signal Strength Indicator (RSSI), turn on power and phase on the one hand, and the distance on the other. The proposed ANN model calculates distance with an average error of 7.31 cm.
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