A Near-Field Focused Array Antenna Empowered by Deep Learning for UHF-RFID Smart Gates

This paper introduces a new Radio Frequency Identification (RFID) gate for access control merging the benefits of Near-Field Focusing (NFF) and Deep Learning (DL). The gate uses a near-field focused antenna with a slight tilted beam to create an asymmetrical reading volume, which is essential to determine the direction of tag transit with a single antenna. The power and phase of the signal backscattered from the tag are used as features for classifying tag status: crossing, static, or moving around the gate yet not crossing it. The antenna is made up of a 3 × 3 array of circularly polarized resonant patches, operating at the ETSI RFID band (865-868 MHz). After validating the coverage volume of the antenna, tag data were used to train a multi-class Support Vector Machine (SVM) and a Long-Short Term Memory (LSTM) Neural Network (LSTM-NN). The appropriately sized LSTM-NN yields 98% classification accuracy in a scenario emulating a realistic shop entrance. The solution offers improved robustness to multipath effects and reduced false positives compared to conventional RFID gates using phased array antennas, two closely spaced portals, or bulky electromagnetic screens or absorbers, at lower cost and with a simpler infrastructure.