标题:Research on Indoor Positioning Technology Based on UWB
作者:Wang, Ning ;Yuan, Xulin ;Ma, Lixin ;Tian, Xincheng
作者机构:[Wang, Ning ;Yuan, Xulin ;Tian, Xincheng ] Shandong University, School of Control Science and Engineering, Jinan; 250061, China;[Ma, Lixin ] Shandong 更多
会议名称:32nd Chinese Control and Decision Conference, CCDC 2020
会议日期:22 August 2020 through 24 August 2020
来源:Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020
出版年:2020
页码:2317-2322
DOI:10.1109/CCDC49329.2020.9164327
关键词:Indoor Positioning; Kalman Filter; Positioning Algorithms; Ranging Fitting Compensation; UWB
摘要:In this paper, the indoor positioning technology based on UWB is studied. The anchor is embedded with POE technology, realizing the integration of long-term stable power supply and data communication. A variety of positioning algorithms are compared and analyzed, including Trilateration algorithm, Fang algorithm, Chan algorithm in the case of 3 anchors and Taylor algorithm, Chan algorithm in the case of multiple anchors. Considering various factors, Taylor algorithm in the case of 4 anchors is finally selected as the core positioning algorithm. The moving average preprocessing of the original ranging data is carried out to reduce the random error. The function of one-key ranging fitting compensation based on least square method is developed, which can reduce the system error and improve the adaptability to different indoor environment. The result of Trilateration algorithm with the lowest complexity is taken as the initial iterative value of Taylor algorithm, which effectively shorten the time of positioning operation. Kalman filter is used to process the positioning results, which greatly improves the positioning stability and dynamic tracking performance. In the laboratory environment, the positioning accuracy of UWB indoor positioning system in this paper can reach ±10cm. © 2020 IEEE.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091570107&doi=10.1109%2fCCDC49329.2020.9164327&partnerID=40&md5=41042afb5c94ff07c1155cbc22300313
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