| LIU Tianmeng(刘天蒙)* **,YANG Hai xiao* **,WU Hong* ** ***.[J].高技术通讯(英文),2024,30(1):61~67 |
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| Two-person device-free localization system based on ZigBee and transformer |
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| DOI：10. 3772 / j. issn. 1006-6748. 2024. 01. 007 |
| 中文关键词: |
| 英文关键词: device-free localization, deep learning, ZigBee |
| 基金项目: |
| Author Name | Affiliation | | LIU Tianmeng(刘天蒙)* ** | (* 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) | | YANG Hai xiao* ** | | | WU Hong* ** *** | |
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| 中文摘要: |
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| 英文摘要: |
| 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 indication (RSSI) and a Transformer network structure. The method aims to address the limited research and low accuracy of two-person device-free localization. This paper first describes the construction of the sensor network used for collecting ZigBee RSSI. It then examines the format and features 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 mapping 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 proposed 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. |
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