Deep learning assisted robust detection techniques for a chipless RFID
  sensor tag
Nadeem Rather Roy B. V. B. Simorangkir John L. Buckley Brendan O'Flynn Salvatore Tedesco
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 large 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.