mm-Wave Chipless RFID Decoding: Introducing Image-Based Deep Learning Techniques
Larry M. Arjomandi, Grishma Khadka, Nemai Chandra Karmakar
Abstract
Chipless RFID tag decoding has some inherent degrees of uncertainty because there is no handshake protocol between chipless tags and readers. This article initially compares the outcome of different pattern recognition methods to decode some frequency-based tags in the mm-wave spectrum. It will be shown that these pattern recognition methods suffer from almost 2%–5% false decoding rate. To overcome this misdecoding problem, two novel methods of making images of the chipless tags are presented. The first method is making 2-D images based on side-looking aperture radar concepts, and the second one is making virtual 2-D images from the 1-D backscattering signals. Then, a 2-D decoding algorithm is suggested based on a convolutional neural network to decode those tag images and compare the results. It is shown that this combined decoding method has very high accuracy, and it almost eliminates any ambiguity and false decoding problems. This is the first time a deep learning method is used with image construction methods to decode chipless RFID tags.