A UHF-RFID Gate Control System Based on a Recurrent Neural Network

被引:11
|
作者
Alvarez-Narciandi, Guillermo [1 ]
Motroni, Andrea [2 ]
Rodriguez Pino, Marcos [1 ]
Buffi, Alice [3 ]
Nepa, Paolo [2 ]
机构
[1] Univ Oviedo, Dept Ingn Elect, Oviedo 33003, Spain
[2] Univ Pisa, Dept Informat Engn, I-56126 Pisa, Italy
[3] Univ Pisa, Dept Energy Syst Terr & Construct Engn, I-56126 Pisa, Italy
来源
IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS | 2019年 / 18卷 / 11期
关键词
Radio frequency identification (RFID) machine learning; recurrent neural network (RNN); RFID neural network; UHF-RFID gate;
D O I
10.1109/LAWP.2019.2929416
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This letter presents a novel, cost-effective, and easy-to-deploy solution to discriminate the direction of goods crossing a UHF radio frequency identification (RFID) gate in a warehouse scenario. The system is based on a grid of UHF-RFID tags deployed on the floor underneath the gate equipped with a single reader antenna. When a transpallet crosses the gate, it shadows the tags of the deployed grid differently, according to the specific direction, namely incoming or outgoing. Such distinguishable signature is employed as input of a recurrent neural network. In particular, the number of readings for each tag is aggregated within short time windows, and a sequence of binary read/missed tag data over the time is extracted. Such temporal sequences are used to train a long short-term memory neural network. Classification performance of the proposed method is shown through a set of measurements in an indoor scenario.
引用
收藏
页码:2330 / 2334
页数:5
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