S11 parameters for 27 tags at 3 tag-to-reader positions (left-middle-right)
Abstract—Chipless RFID tag decoding has some inherent degrees of uncertainty because there is no handshake protocol between chipless tags and readers. This paper 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 to 5% false decoding rate. To overcome this mis-decoding problem, two novel methods of making images of the chipless tags are presented. The first method is using continuous wave imaging based on side looking aperture radar concepts, and the second one is making virtual 2-D images from 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 removes any ambiguity and false decoding problems. This is the first time a deep-learning method is used with image-construction methods to decode frequency-based chipless tags. Index Terms—RFID, Side Looking Aperture Radar, Synthetic Aperture Radar, chipless RFID tags, chipless sensors, pattern recognition, convolutional neural network, Deep-learning, mmwave band.
This is the S11 parameters collected for 27 alphanumeric tags, with background noise deleted. Tag to antenna is 5 cm, a horn antenna (A-info antenna in the paper reference)
Tags are scanned in three positions, perpendicular to the antenna broadside, and +/-1.5cm away as right of left. Noise is already deducted from the data.
As data is huge (<1M rows), it has been put into two sheets.
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