Deep Convolutional Neural Network for Passive RFID Tag Localization Via Joint RSSI and PDOA Fingerprint Features

Abstract

Radio-frequency identification (RFID) localization has drawn much attention with the emergence of the Internet of Things (IoT). Deep learning with applications to RFID localization owns a lot of advantages. In this paper, we present a deep convolutional neural network (CNN)-based approach for passive RFID tag localization exploiting joint fingerprint features of the received signal strength indication (RSSI) and phase difference of arrival (PDOA). First, the RSSI and PDOA data are extracted from the received signals of RFID readers. Then, a CNN with three convolution layers and pooling layers is design, in which the normalized RSSI and PDOA data are formed into images as its input to train the weights in the offline stage. In the online stage, the RSSI and PDOA data of test tags are collect and then the positions of unknown tags is predected based on the designed CNN. In the simulations, the accuracy of the proposed approach is compared with several fingerprinting-based schemes such as LANDMARC, weighted K-nearest neighbor (WKNN) and deep neural network (DNN), and the impact of different fingerprint data sets and noise variances on the positioning accuracy is analyzed. Experiments show that the proposed approach can locate multiple tags with high accuracy and stability in complex indoor environment, and outperforms other existing schemes.

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“…erefore, in the CASC model, the convolutional self-encoder is used to embed the obtained high-dimensional document matrix into the lowdimensional potential vector space through training and learning, so as to reduce the vector dimension and preserve the internal structure of the original data to the greatest extent, so as to shorten the time required for clustering. After embedding the document matrix into the low-dimensional potential space, the obtained low-dimensional vector representation is used for spectral clustering, and then the final clustering result is obtained [22][23][24][25].…”

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“…These results can also be logged to allow for deep learning to distinguish and observe any hidden patterns, as when deep CNN is applied to RFID multi-tag localization with the joint fingerprint features of the RSSI and the phase difference of arrival (PDOA). The CNN for RFID localization has great advantages, such as the capability of processing a large amount of data, extracting and training fingerprint features, sharing the parameter structure and reducing the complexity of the neural network [22]. The model comparison results are shown in Table 5.…”

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