Machine learning approaches for EM signature analysis in chipless RFID technology
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Accepted Version
Date
2024
Authors
Rather, Nadeem
Simorangkir, Roy B. V. B.
Buckley, John L.
O’Flynn, Brendan
Tedesco, Salvatore
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published Version
Abstract
In this paper, for the first time, we provide a comprehensive review of Machine Learning (ML) approaches in Chipless Radio Frequency Identification (CRFID) technology, which is a fast-developing sector with applications in inventory management, anti-counterfeiting, health monitoring, and environmental monitoring, to name a few. ML techniques are rapidly being integrated to improve CRFID systems’ capabilities for robust detection of information. The combination of ML with CRFID technology is presented, examining various ML approaches, applications, challenges, and future perspectives. It is observed that ML has been successfully deployed in CRFID with high accuracy in the detection of information from CRFID tags. Challenges, such as data quality, security, and scalability are identified. Moreover, the literature currently struggles in the application of ML models on high-capacity tags, and lacks standardized data collection and sharing methodologies. We suggest the development of common data collection protocols, data sharing initiatives, and collaboration to establish a cohesive framework for CRFID data-driven research.
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Keywords
Chipless RFID , Deep learning , Electromagnetics , Machine learning , Radar cross section , RFID
Citation
Rather, N., Simorangkir, R. B. V. B., Buckley, J. L., O’Flynn, B. and Tedesco, S. (2024) 'Machine learning approaches for EM signature analysis in chipless RFID technology', 2024 18th European Conference on Antenna and Propagation (EuCAP 2024), Glasgow, Scotland, 17-22 March.
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© 2024, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.