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

In this article, we present a new approach for robust reading of identification (ID) and sensor data from chipless radio frequency ID (CRFID) sensor tags. For the first time, machine-learning (ML) and deep-learning (DL) regression modeling techniques are applied to a dataset of measured radar cross Section (RCS) data that have been derived from large-scale robotic measurements of custom-designed, 3-bit CRFID sensor tags. The robotic system is implemented using the first-of-its-kind automated data acquisition method using an ur16e industry-standard robot. A dataset of 9600 electromagnetic (EM) RCS signatures collected using the automated system is used to train and validate four ML models and four 1-D convolutional neural network (1-D CNN) architectures. For the first time, we report an end-to-end design and implementation methodology for robust detection of 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 generalize well on the given data. However, the 1-D CNN models outperformed the conventional ML models in the detection of ID and sensing values. The best 1-D CNN model architectures performed well with a low root-mean-square error (RMSE) of 0.061 (0.87%) for tag ID and 0.0241 (3.44%) error for capacitive sensing.