Deep Learning Assisted Robust Detection Techniques for a Chipless RFID Sensor Tag

Rather, Nadeem; Simorangkir, Roy B. V. B.; Buckley, John L.; O’Flynn, Brendan; Tedesco, Salvatore

Abstract

In this paper, we present a new approach for robust reading of identification and sensor data from chipless RFID sensor tags. For the first time, Machine Learning (ML) and Deep Learning (DL) regression modelling techniques are applied to a dataset of measured Radar Cross Section (RCS) data that has been derived from large-scale robotic measurements of custom-designed, 3-bit chipless RFID sensor tags. The robotic system is implemented using the first-of-its-kind automated data acquisition method using an ur16e industry-standard robot. A data set of 9,600 Electromagnetic (EM) RCS signatures collected using the automated system is used to train and validate four ML models and four 1-dimensional Convolutional Neural Network (1D CNN) architectures. For the first time, we report an end-to-end design and implementation methodology for robust detection of identification (ID) and sensing data using ML/DL models. Also, we report, for the first time, the effect of varying tag surface shapes, tilt angles, and read ranges that were incorporated into the training of models for robust detection of ID and sensing values. The results show that all the models were able to generalise well on the given data. However, the 1D CNN models outperformed the conventional ML models in the detection of ID and sensing values. The best 1D CNN model architectures performed well with a low Root Mean Square Error (RSME) of 0.061 (0.87%) for tag ID and 0.0241 (3.44%) error for the capacitive sensing.

Citation

Rather, N., Simorangkir, R. B. V. B., Buckley, J. L., O’Flynn, B., & Tedesco, S. (2023). Deep Learning Assisted Robust Detection Techniques for a Chipless RFID Sensor Tag. IEEE Transactions on Instrumentation and Measurement, https://doi.org/10.1109/tim.2023.3334378

• Journal Article Type: Article
• Acceptance Date: Oct 26, 2023
• Online Publication Date: Nov 28, 2023
• Publication Date: 2023
• Deposit Date: Dec 6, 2023
• Publicly Available Date: Dec 6, 2023
• Journal: IEEE Transactions on Instrumentation and Measurement
• Print ISSN: 0018-9456
• Electronic ISSN: 1557-9662
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1109/tim.2023.3334378
• Public URL: https://durham-repository.worktribe.com/output/1982681