Chipless RFID Tag Detection Based on Continuous Wavelet Transform and Convolutional Neural Networks

In this work, we propose a novel approach for the detection of chipless radio frequency identification (RFID) signals. The method is based on the application of transformations to the measurement, in conjunction with the utilization of Artificial Intelligence (AI) algorithms. In the initial stage of the process, frequency-related features are measured. Subsequently, a time-frequency representation of these measurements is generated through the application of the inverse Fourier transform (IFT), a time-gating strategy, and the continuous wavelet transform (CWT). The resulting representation is then used as input to a shallow convolutional neural network (CNN), which is able to learn complex patterns while being able to generalize to new measurements. Furthermore, the proposed scheme incorporates a filtering process, based on the probabilities derived from the model, to filter out low-confidence predictions. To assess the performance of the proposed method, we consider a population of 16 tags. We collected 4800 measurements for the training phase and 2400 measurements for the testing phase in a real-world environment. These measurements are recorded on different days and within a distance range of 50–140 cm from the tag to the antenna. The proposed method exhibited accuracies of 94% in the 110–140 cm range, 99% in the 80–110 cm range, and 100% in the 50–80 cm range, showcasing its suitability for chipless RFID detection. All the code and datasets used in this work are publicly available on GitHub and the IEEE dataport, respectively.