Chipless RFID Tag Implementation and Machine Learning Workflow for Robust Identification

In this work, we describe step by step a complete workflow to apply machine learning (ML) classification for chipless RFID tag identification, covering: i) the tag implementation criteria for circular ring resonator (CRR) arrays for ML interoperability; ii) the data collection procedure to get a sufficiently representative dataset of real measurements; iii) the ML techniques to visualize the data and reduce its dimensionality; iv) the evaluation of the ML classifier to ensure high accuracy predictions on new measurements; and v) a thresholding scheme to increase the certainty of the predictions. The differences of the tags’ frequency responses are maximized by optimizing the Hamming distance between the tag identifiers and by controlling the radar cross section (RCS) level of each CRR array. We show on two scenarios, fixed and flexible range (up to 160 cm), that the proposed workflow can achieve perfect accuracy in most cases for the identification of four tags.