摘要
针对卫星钟差预报模型的普遍适用性低,以及预报模型中星载原子钟类型和建模特点结合不充分等问题,提出了四种适用于非线性处理的神经网络模型来预报卫星钟差.首先对钟差数据进行预处理;然后通过基于萤火虫算法(firefly algorithm,FA)优化反向传播(back propagation,BP)神经网络(FA-BP neural networks,FA-BPNN)模型、Elman循环神经网络模型、径向基函数(radial basis function,RBF)神经网络模型以及基于卷积神经网络-长短期记忆(convolutional neural networks-long short term memory,CNN-LSTM)网络模型对1 d和7 d的钟差数据量建立模型;再采用武汉大学国际GNSS服务(International GNSS Service,IGS)数据分析中心(WHU)的GPS精密钟差数据进行钟差预报;最后从不同建模数据量及不同批次卫星的同一类型原子钟和不同批次卫星的不同类型原子钟的角度,将预报效果进行分析与对比.结果表明:1)四种模型在建模特点上,1 d的钟差数据量建模精度均比7 d的钟差数据量建模预报精度高.其中,RBF神经网络模型随着钟差建模数据增加时,预报精度影响变大,预报精度从亚纳秒量级变化到几十纳秒量级.2)四种神经网络模型预报精度与卫星在轨运行时长以及星载原子钟类型相关;在轨运行时间长的卫星其预报的性能不一定差,不同批次卫星的不同类型原子钟的预报效果的性能可能一样;其铯原子钟类型卫星在四种神经网络模型预报中精度最好.
Aiming at the problems of the low applicability of the satellite clock error prediction model and the insufficient combination of the type of the satellite-borne atomic clock and the modeling characteristics in the prediction model,four kinds of neural network models suitable for nonlinear processing are proposed to predict satellite clock error.Firstly,the clock error data is preprocessed.Then,the firefly algorithm models were established by using the back-propagation(FA-BPNN)model,the Elman cyclic(Elman)model,the radial basis function(RBF)model,and the convolutional neural network data of 1 d and 7 d based on the CNN-LSTM model GPS precise clock error data from the Wuhan University International GNSS service(IGS)data analysis center(WHU)are used for clock error prediction At last,the effect of the prediction is analyzed and compared from the point of view of different modeling data and different batches of satellites with the same type of atomic clock and different batches of satellites with different types of atomic clock.The results show that:1)the modeling accuracy of 1 d clock error data is higher than that of 7 d clock error data,and the RBF model has the greatest influence on the prediction accuracy with the increase of clock error data,and the prediction accuracy changes from sub-nanosecond to tens of nanosecond.2)the prediction accuracy of the four neural network models is related to the satellite operating time in orbit and the type of atomic clock on board.The prediction performance of the satellites with long operating time in orbit is not necessarily bad,and the prediction performance of different types of atomic clock on different batches of satellites may be the same.The cesium atomic clock type satellite has the best prediction accuracy among the four neural network models.
作者
卢玉皖
郑礼全
胡超
LU Yuwan;ZHENG Liquan;HU Chao(School of Spatial Information and Geomatics Engineering,Anhui University of Science and Technology,Huainan 232001,China;Key Laboratory of Aviation-aerospace-ground Cooperative Monitoring and Early Warning of Coal Mining-induced Disasters of Anhui Highter Education Institutes,Anhui University of Science and Technology,Huainan 232001,China;Coal Industry Enagineering Research Center of Mining Area Environmental and Disaster Cooperative Monitoring,Anhui University of Science and Technology,Huainan 232001,China)
出处
《全球定位系统》
CSCD
2023年第5期46-55,91,共11页
Gnss World of China
基金
安徽省自然科学基金(2108085QD173)
安徽省教育厅自然科学项目(KJ2020A0310)。
关键词
GPS
卫星钟差
深度学习
神经网络模型
卫星钟差预报
GPS
satellite clock error
deep learning
neural network model
satellite clock error forecast
