Read/Interrogation Enhancement of Chipless RFIDs Using Machine Learning Techniques

Abstract

This letter describes the implementation of a machine learning (ML) classification strategy for read/interrogation enhancement in chipless radio frequency identification (RFID) applications. A novel ML-based approach for classification and of detection tag identifications (IDs) has been presented, which can perform effective transponder readings for a wide variety of ranges and contexts, while providing tag-ID detection accuracy of up to 99.3%. Four tags encoding the four 2 bit IDs were inkjet-printed onto flexible low-cost polyethylene terephtalate substrates and interrogated without crosstalk or clutter interference de-embedding at ranges up to 50 cm, with different orientations and with and without the presence of scattering objects in the vicinity of the tags and reader. A support vector machine algorithm was then trained using 816 measurements, and its accuracy was tested and characterized as a function of the included training data. Finally, the excellent performance of the approach, displaying reading accuracies ranging from89.6% to 99.3%, is reported. This effort sets a precedent, opening the door to a rich and wide area of research for the implementation of ML methods for the enhancement of chipless RFID applications.