{"title":"UHF RFID Tags Zoning Using Harmonic Signals and Machine Learning","authors":"Dahmane Allane;Christophe Loussert;Yvan Duroc;Smail Tedjini","doi":"10.1109/JRFID.2023.3306025","DOIUrl":null,"url":null,"abstract":"This paper describes an effective method for improving the detection of UHF RFID (Ultra High Frequency Radio Frequency Identification) tags in a restricted area. The so-called zoning technique is a recurring problem in practical RFID applications: it consists in detecting within an environment with multiple tags that are exclusively present in the zone of interest. The proposed method is based on the concept of Nth harmonic, a new paradigm that involves utilizing the harmonic signals backscattered by tags. Such a method is coupled with a machine learning technique. Experimental results show the importance of harmonic features for better tags zoning. Using a four-layer CNN classifier, we can achieve 99% prediction accuracy by leaving a keep-out distance of 0.5 m between two zones, using the harmonic RSSI (Received Signal Strength Indicator) sum feature, and 94.7% by using the best feature at \n<inline-formula> <tex-math>$f_{0}$ </tex-math></inline-formula>\n, which is the RSSI max, achieving around 5 times less prediction errors. Furthermore, combining the harmonic and fundamental features leverage the prediction accuracy to 99.8%.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE journal of radio frequency identification","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10227283/","RegionNum":0,"RegionCategory":null,"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 paper describes an effective method for improving the detection of UHF RFID (Ultra High Frequency Radio Frequency Identification) tags in a restricted area. The so-called zoning technique is a recurring problem in practical RFID applications: it consists in detecting within an environment with multiple tags that are exclusively present in the zone of interest. The proposed method is based on the concept of Nth harmonic, a new paradigm that involves utilizing the harmonic signals backscattered by tags. Such a method is coupled with a machine learning technique. Experimental results show the importance of harmonic features for better tags zoning. Using a four-layer CNN classifier, we can achieve 99% prediction accuracy by leaving a keep-out distance of 0.5 m between two zones, using the harmonic RSSI (Received Signal Strength Indicator) sum feature, and 94.7% by using the best feature at
$f_{0}$
, which is the RSSI max, achieving around 5 times less prediction errors. Furthermore, combining the harmonic and fundamental features leverage the prediction accuracy to 99.8%.