Two-person device-free localization system based on ZigBee and transformer

• Published in: 高技术通讯（英文版） Vol. 30; no. 1; pp. 61 - 67
• Main Authors: LIU Tianmeng(刘天蒙), YANG Haixiao, WU Hong
• Format: Journal Article
• Language: English
• Published: Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology,Nankai University,Tianjin 300350,P.R.China%School of Electronic Information and Optical Engineering,Nankai University,Tianjin 300350,P.R.China 01.03.2024; 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
• Subjects: deep learning; ZigBee; device-free localization
• ISSN: 1006-6748
• DOI: 10.3772/j.issn.1006-6748.2024.01.007

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.