Complex Natural Resonance-Based Chipless RFID Multi-Tag Detection Using One-Dimensional Convolutional Neural Networks

This paper proposes a chipless radio frequency identification (RFID) multi-tag detection system. The one-dimensional convolutional neural network (1D CNN) was employed as an intelligent classifier of the proposed system, which was fed by complex natural resonances (CNRs). Experiments with contexts of a single chipless RFID tag were conducted to collect input datasets for training and validating the 1D CNN. The CNRs and natural frequencies only extracted from the individual tag’s responses by using the short-time matrix pencil method (STMPM) and then obtained after performing data augmentation were separately fed to the 1D CNN for training and validation in order to compare their performance. The accuracy obtained from the training and validation of the 1D CNN fed by the CNRs was significantly higher than that of the 1D CNN fed by the frequencies only. The performance matrices in terms of precision, recall, and F1-score also confirmed the superiority of the use of CNRs over that of frequencies only. In order to verify the performance of real-time multi-tag detection utilizing the proposed system, experiments with contexts of multiple tags were carried out, and the experimental results have shown that the system using the 1D CNN fed by the frequency only failed to detect multiple tags. In contrast, the proposed system was able to deliver 100% accurate multi-tag detection. However, as demonstrated by the experimental results, the proposed chipless RFID multi-tag detection system was restricted to a resolution of 3 cm.