Machine Learning-Based Identification and Localization of Closely Spaced Chipless RFID Tags

Abstract

We present a machine learning-based method for imaging and resolving multiple closely spaced chipless radio frequency identification (RFID) tags within a read zone. Backscattered signal measurements are acquired via a 2-D raster scan using a directive reader antenna and a trained classifier is used to extract the spectral contributions of the tags at each spatial position. This enables the reconstruction of color-coded probability maps that reflect the likely identity and location of each tag. We demonstrate the ability to automatically identify and localize pairs of chipless RFID tags selected from three tag types with distinct resonance frequencies in the 2–6 GHz range. Our method achieves 97.82% accuracy with magnitude measurements and 100% with phase, with average positional errors of less than 6.4 mm when using phase data, even when the tags are positioned at separations below 0.6$\lambda$, where mutual coupling is strong. Current limitations and future directions are also discussed.