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Two-person device-free localization system based on ZigBee and transformer
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Most studies on device-free localization currently focus on single-person scenarios.This paper proposes a novel method for device-free localization that utilizes ZigBee received signal strength indi-cation(RSSI)and a Transformer network structure.The method aims to address the limited re-search and low accuracy of two-person device-free localization.This paper first describes the con-struction of the sensor network used for collecting ZigBee RSSI.It then examines the format and fea-tures of ZigBee data packages.The algorithm design of this paper is then introduced.The box plot method is used to identify abnormal data points,and a neural network is used to establish the map-ping model between ZigBee RSSI matrix and localization coordinates.This neural network includes a Transformer encoder layer as the encoder and a fully connected network as the decoder.The pro-posed method's classification accuracy was experimentally tested in an online test stage,resulting in an accuracy rate of 98.79%.In conclusion,the proposed two-person localization system is novel and has demonstrated high accuracy.
device-free localizationdeep learningZigBee
LIU Tianmeng(刘天蒙)、YANG Haixiao、WU Hong
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School of Electronic Information and Optical Engineering,Nankai University,Tianjin 300350,P.R.China
Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology,Nankai University,Tianjin 300350,P.R.China
Engineering Research Center of Thin Film Optoelectronics Technology,Nankai University,Tianjin 300350,P.R.China
National Natural Science Foundation of ChinaNational Natural Science Foundation of China
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