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ELoran Propagation Delay Prediction Model Based on a BP Neural Network for a Complex Meteorological Environment

文献类型：期刊论文

作者Liu, Shiyao1,2; Guo, Wei1,2; Hua, Yu1,2; Kou, Wudian1,2
刊名SENSORS
出版日期2023-05-29
卷号23期号:11页码:25
关键词eLoran meteorological factor propagation delay prediction model Back-Propagation neural network
DOI10.3390/s23115176
英文摘要The core of eLoran ground-based timing navigation systems is the accurate measurement of groundwave propagation delay. However, meteorological changes will disturb the conductive characteristic factors along the groundwave propagation path, especially for a complex terrestrial propagation environment, and may even lead to microsecond-level propagation delay fluctuation, seriously affecting the timing accuracy of the system. Aiming at this problem, this paper proposes a propagation delay prediction model based on a Back-Propagation neural network (BPNN) for a complex meteorological environment, which realizes the function of directly mapping propagation delay fluctuation through meteorological factors. First, the theoretical influence of meteorological factors on each component of propagation delay is analyzed based on calculation parameters. Then, through the correlation analysis of the measured data, the complex relationship between the seven main meteorological factors and the propagation delay, as well as their regional differences, are demonstrated. Finally, a BPNN prediction model considering regional changes of multiple meteorological factors is proposed, and the validity of the model is verified by long-term collected data. Experimental results show that the proposed model can effectively predict the propagation delay fluctuation in the next few days, and its overall performance is significantly improved compared with that of the existing linear model and simple neural network model.
WOS关键词WAVE-PROPAGATION ; GROUND-WAVE
资助项目National Natural Science Foundation of China[11803040] ; Key Research Program of Frontier Sciences of CAS[QYZDJ-SSW-JSC034]
WOS研究方向Chemistry ; Engineering ; Instruments & Instrumentation
语种英语
WOS记录号WOS:001005756900001
出版者MDPI
资助机构National Natural Science Foundation of China ; National Natural Science Foundation of China ; Key Research Program of Frontier Sciences of CAS ; Key Research Program of Frontier Sciences of CAS ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Key Research Program of Frontier Sciences of CAS ; Key Research Program of Frontier Sciences of CAS ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Key Research Program of Frontier Sciences of CAS ; Key Research Program of Frontier Sciences of CAS ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Key Research Program of Frontier Sciences of CAS ; Key Research Program of Frontier Sciences of CAS
源URL[http://210.72.145.45/handle/361003/14202]  
专题国家授时中心_授时方法与技术研究室
通讯作者Liu, Shiyao
作者单位1.Chinese Acad Sci, Key Lab Precise Positioning & Timing Technol, Xian 710600, Peoples R China
2.Chinese Acad Sci, Natl Time Serv Ctr, Xian 710600, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Shiyao,Guo, Wei,Hua, Yu,et al. ELoran Propagation Delay Prediction Model Based on a BP Neural Network for a Complex Meteorological Environment[J]. SENSORS,2023,23(11):25.
APA Liu, Shiyao,Guo, Wei,Hua, Yu,&Kou, Wudian.(2023).ELoran Propagation Delay Prediction Model Based on a BP Neural Network for a Complex Meteorological Environment.SENSORS,23(11),25.
MLA Liu, Shiyao,et al."ELoran Propagation Delay Prediction Model Based on a BP Neural Network for a Complex Meteorological Environment".SENSORS 23.11(2023):25.

入库方式： OAI收割

来源：国家授时中心

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