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基于光纤复用技术的物联网节点定位研究 被引量:4

Research on node localization of Internet of things based on fiber multiplexing technology
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摘要 传统物联网节点定位方法易受噪声干扰,导致定位时延较高、精度较小。提出基于光纤复用技术的物联网节点定位研究方法,建立基于光纤复用技术的物联网,采用中值滤波法去除物联网中的噪声后,建立节点信号光纤衰减模型削弱外界干扰噪声,在此基础上使用三角定位法获取物联网节点的初始位置信息,依据该信息通过无损卡尔曼算法完成物联网节点的精确定位。实验结果表明,所提方法可有效定位基于光纤复用技术的物联网节点,且误差范围处于[0.2 m,0. 39 m]区间内,误差概率仅为2%;定位时延均值为2 s,具有较高的定位效率和精度。 When the weighted centroid location correction method is used to locate the nodes of the Internet of things, the time delay and precision of the location are relatively low due to noise interference. This paper puts forward an approach to the location of nodes in the Internet of things based on fiber multiplexing technology. An Internet of things based on fiber multiplexing technology is established, the noise in the Internet of things is removed by median filtering method, and a node signal attenuation model is established to weaken external interference noise. On these basis, the initial location information of the Internet of things node is obtained by triangulation method, and the precise location of the Internet of things node is achieved by lossless Kalman algorithm. The experimental results show that the proposed method can effectively locate the Internet of things nodes based on fiber multiplexing technology, and the er ror range is within the interval [ 0. 2m, 0. 39m], the error probability is only 2 % and the mean time delay of positio- ning is 2 s, which has high positioning efficiency and precision.
作者 刘志龙 杜远坤 张淋江 李志伟 LIU Zhilong;DU Yuankun;ZHANG Linjiang;LI Zhiwei(Network Management Center, Henan University of Animal Husbandry and Economy, Zhengzhou 450011, China;Zhengzhou College of Science & Technology, Department of Information Engineering, Zhengzhou 450064, China)
出处 《激光杂志》 北大核心 2019年第7期79-82,共4页 Laser Journal
基金 河南省(No.2017SJGLX500) 河南省高等学校重点科研项目课题(No.14A520014) 郑州市科技计划项目课题(No.20140670)
关键词 光纤复用技术 物联网 节点定位 中值滤波 噪声 无损卡尔曼算法 fiber optic multiplexing technology Internet of things node positioning median filtering noise non-destructive Kalman algorithm
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