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Abstract

The three-dimensional (3D) coordinate measurement of radio frequency identification (RFID) multi-tag

networks is one of the important issues in the field of RFID, which affects the reading performance of

RFID multi-tag networks. In this paper, a novel method for 3D coordinate measurement of RFID multitag

networks is proposed. A dual-CCD system (vertical and horizontal cameras) is used to obtain images of

RFID multi-tag networks from different angles. The iterative threshold segmentation and the morphological

filtering method are used to process the images. The template matching method is respectively used to

determine the two-dimensional (2D) coordinate and the vertical coordinate of each tag. After that, the

3D coordinate of each tag is obtained. Finally, a back-propagation (BP) neural network is used to model

the nonlinear relationship between the RFID multi-tag network and the corresponding reading distance.

The BP neural network can predict the reading distances of unknown tag groups and find out the optimal

distribution structure of the tag groups corresponding to the maximum reading distance. In the future work,

the corresponding in-depth research on the neural network to adjust the distribution of tags will be done.

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Authors and Affiliations

Zhuang Xiao
Xiaolei Yu
Zhimin Zhao
Wenjie Zhang
Zhenlu Liu
Dongsheng Lu
Dingbang Dong

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