基于球谐函数,实现区域电离层建模,并对区域差分码偏差(differential code bias,DCB)与总电子含量(total electron content,TEC)进行解算。对于格网处垂直总电子含量(vertical total electron content,VTEC)出现的异常值,提出一种序列...基于球谐函数,实现区域电离层建模,并对区域差分码偏差(differential code bias,DCB)与总电子含量(total electron content,TEC)进行解算。对于格网处垂直总电子含量(vertical total electron content,VTEC)出现的异常值,提出一种序列无约束最小化技术(sequential unconstrained minimization technique,SUMT)修正法进行修正,利用国际全球导航卫星系统服务(International GNSS Service,IGS)网络的6个测站双频观测数据,建立了电离层VTEC区域模型,并估算了31天的卫星频间DCB,将估算值与电离层分析中心中国科学院(Chinese Academy of Sciences,CAS)发布的产品进行对比分析,结果显示:所有的卫星差值都在0.42 ns以内,其中87.5%的卫星差值在0.4 ns以内,78.1%的卫星差值在0.2 ns以内,频间DCB的平均偏差基本小于0.4 ns。此外,估算的全球定位系统(global positioning system,GPS)卫星DCB序列的标准差(standard deviation,STD)值小于0.1 ns。建立了经纬度范围为5°E~25°E、40°N~60°N的电离层区域模型,将VTEC建模结果与CAS发布的全球电离层地图(global ionospheric map,GIM)产品做差比较,结果显示整体时间点的差值均处于4 TECU以内,且超过90%的区域差值在2 TECU以内,表明估算的结果与CAS产品具有良好的一致性。展开更多
电离层总电子含量TEC的监测与预报是近地空间环境研究的重要内容,对卫星通讯和导航定位等有重要意义。使用基于Transformer(变形金刚)的iInformer(告密者)模型,提出中国区域电离层TEC短期预报新方法,且分别对磁静期与磁暴期电离层进行...电离层总电子含量TEC的监测与预报是近地空间环境研究的重要内容,对卫星通讯和导航定位等有重要意义。使用基于Transformer(变形金刚)的iInformer(告密者)模型,提出中国区域电离层TEC短期预报新方法,且分别对磁静期与磁暴期电离层进行预测。为了分析短期电离层新模型预测效果,选取神经网络模型、线性模型、长短时记忆模型进行对比。结果表明,磁静期选定区域内iInformer模型有效适用于短期预测任务且预测精度明显优于其他对比模型,均方根误差在3个区域均低于1.45 TECU(total electron content units,总电子含量单位)。iInformer模型在应对不同数据量时,均能保持稳定的预测性能。特别是在数据集数量相对有限(少于2个月)的情况下,iInformer模型的预报精度显著优于其他模型。相较于单一数据源,多数据源下的iInformer模型预测精度有显著提升,提升幅度在2%~7.4%。展开更多
基于时间序列方法能够对短时间的电离层总电子含量(Total Electron Content,TEC)进行较好预测,但由于电离层TEC受各种因素影响,直接使用原始TEC序列数据会受到各种噪声的干扰,影响其预测精度。本文利用小波方法的良好去噪效果,提出一种...基于时间序列方法能够对短时间的电离层总电子含量(Total Electron Content,TEC)进行较好预测,但由于电离层TEC受各种因素影响,直接使用原始TEC序列数据会受到各种噪声的干扰,影响其预测精度。本文利用小波方法的良好去噪效果,提出一种基于小波去噪和时间序列分析的电离层TEC组合模型预测方法,采用欧洲定轨中心(CODE)发布的2021年数据对其进行分析。结果表明,对于高、中低纬度,使用组合模型的预测精度分别为96.14%、92.34%和85.09%。与传统的时间序列方法预测的结果相比,在高、中纬度的精度都有所提高,而低纬度精度相当,实验结果可验证本方法的有效性。展开更多
Subspace-based signal processing methods are fundamentally pre-trained Artificial Neural Networks(ANN)that provide the basic structure for numerous computer vision applications and explore the most promising Earth Obs...Subspace-based signal processing methods are fundamentally pre-trained Artificial Neural Networks(ANN)that provide the basic structure for numerous computer vision applications and explore the most promising Earth Observation Applications(EOA).This paper examines the fundamentals of subspacebased methods and explores the most promising algorithm for forecasting ionospheric signal delays,which was designed explicitly regarding signal and noise subspaces.The learning efficiency derived from the subspace-based components of Singular Spectrum Analysis(SSA)significantly influences the implementation of Linear Recurrent Formula(LRF)and ANN models.The proposed study introduces a novel enhancement to LRF and ANN methodologies for Global Positioning System(GPS)-Total Electron Content(TEC)forecasts based on SSA.The GPS-derived TEC at Bangalore(13.02°N and 77.57°E)location grid during sunspot cycle 25(2020)is considered for analysis.The SSA-LRF-ANN model demonstrates superior accuracy compared with the SSA-LRF,Autoregressive Moving Average(ARMA),and Holt-Winter(HW)models,achieving a correlation of 0.99,a Mean Absolute Error(MAE)of 0.55 TECU,a Mean Absolute Percentage Error(MAPE)of 7.06%,and a Root Mean Square Error(RMSE)of 0.75 TECU.Furthermore,the results and discussions section presents numerical illustrations that showcase the practical implementation of the SSA-LRF-ANN and its application.展开更多
In recent years,GNSS-derived total electron content(TEC)measurements have emerged as an effective method for detecting natural hazards through their ionospheric manifestations.Seismo-atmospheric disturbances generated...In recent years,GNSS-derived total electron content(TEC)measurements have emerged as an effective method for detecting natural hazards through their ionospheric manifestations.Seismo-atmospheric disturbances generated by earthquakes,tsunamis,and volcanic eruptions propagate as traveling ionospheric disturbances(TIDs)that modify ionospheric electron density.Despite this potential,specialized open-source tools for such analyses remain limited.We present IonKit-NH,a MATLAB-based toolkit enabling systematic processing of multi-GNSS data(GPS,GLONASS,Galileo,BDS)through dual-frequency combination analysis for TEC derivation.The software implements automated generation of time-distance diagrams and 2D TEC perturbation maps,enabling quantitative characterization of TID propagation parameters associated with natural hazards.This toolkit enhances standardized analysis of ionospheric precursors and co-seismic signals across global navigation satellite systems.展开更多
电离层总电子含量(Total Electron Content,TEC)精确预报对提高卫星导航定位精度具有重要意义.为此,提出一种联合鲸鱼优化算法(Whale Optimization Algorithm,WOA)与长短期记忆神经网络模型(Long-Short Term Memory Networks,LSTM)的TE...电离层总电子含量(Total Electron Content,TEC)精确预报对提高卫星导航定位精度具有重要意义.为此,提出一种联合鲸鱼优化算法(Whale Optimization Algorithm,WOA)与长短期记忆神经网络模型(Long-Short Term Memory Networks,LSTM)的TEC短期预报模型;该模型通过LSTM模型训练得到WOA算法的最佳适应度,并利用优化的WOA算法得到LSTM模型最优参数.最后,结合欧洲定轨中心(Center for Orbit Determination in Europe,CODE)提供的TEC格网点数据对所提模型进行验证;试验结果表明:地磁平静状态下,组合模型的平均相关系数ρ较LSTM模型在低、中、高纬度分别提升了2.8%、6.2%和14.8%;地磁活跃状态下组合模型的平均相关系数ρ在低、中、高纬度地区较LSTM模型分别提升了6.6%、9.2%与7.9%.且模型预报效果与地磁活跃状态、季节、太阳活跃水平等有关,在不同地磁活跃状态、季节与不同太阳活动水平情况下,组合模型预报效果均优于单一LSTM模型,为电离层TEC预报模型的实际应用提供了参考.展开更多
文摘电离层总电子含量TEC的监测与预报是近地空间环境研究的重要内容,对卫星通讯和导航定位等有重要意义。使用基于Transformer(变形金刚)的iInformer(告密者)模型,提出中国区域电离层TEC短期预报新方法,且分别对磁静期与磁暴期电离层进行预测。为了分析短期电离层新模型预测效果,选取神经网络模型、线性模型、长短时记忆模型进行对比。结果表明,磁静期选定区域内iInformer模型有效适用于短期预测任务且预测精度明显优于其他对比模型,均方根误差在3个区域均低于1.45 TECU(total electron content units,总电子含量单位)。iInformer模型在应对不同数据量时,均能保持稳定的预测性能。特别是在数据集数量相对有限(少于2个月)的情况下,iInformer模型的预报精度显著优于其他模型。相较于单一数据源,多数据源下的iInformer模型预测精度有显著提升,提升幅度在2%~7.4%。
文摘基于时间序列方法能够对短时间的电离层总电子含量(Total Electron Content,TEC)进行较好预测,但由于电离层TEC受各种因素影响,直接使用原始TEC序列数据会受到各种噪声的干扰,影响其预测精度。本文利用小波方法的良好去噪效果,提出一种基于小波去噪和时间序列分析的电离层TEC组合模型预测方法,采用欧洲定轨中心(CODE)发布的2021年数据对其进行分析。结果表明,对于高、中低纬度,使用组合模型的预测精度分别为96.14%、92.34%和85.09%。与传统的时间序列方法预测的结果相比,在高、中纬度的精度都有所提高,而低纬度精度相当,实验结果可验证本方法的有效性。
文摘Subspace-based signal processing methods are fundamentally pre-trained Artificial Neural Networks(ANN)that provide the basic structure for numerous computer vision applications and explore the most promising Earth Observation Applications(EOA).This paper examines the fundamentals of subspacebased methods and explores the most promising algorithm for forecasting ionospheric signal delays,which was designed explicitly regarding signal and noise subspaces.The learning efficiency derived from the subspace-based components of Singular Spectrum Analysis(SSA)significantly influences the implementation of Linear Recurrent Formula(LRF)and ANN models.The proposed study introduces a novel enhancement to LRF and ANN methodologies for Global Positioning System(GPS)-Total Electron Content(TEC)forecasts based on SSA.The GPS-derived TEC at Bangalore(13.02°N and 77.57°E)location grid during sunspot cycle 25(2020)is considered for analysis.The SSA-LRF-ANN model demonstrates superior accuracy compared with the SSA-LRF,Autoregressive Moving Average(ARMA),and Holt-Winter(HW)models,achieving a correlation of 0.99,a Mean Absolute Error(MAE)of 0.55 TECU,a Mean Absolute Percentage Error(MAPE)of 7.06%,and a Root Mean Square Error(RMSE)of 0.75 TECU.Furthermore,the results and discussions section presents numerical illustrations that showcase the practical implementation of the SSA-LRF-ANN and its application.
基金supported by National Natural Science Foundation of China(Grant No.42274017)Guangdong Basic and Applied Basic Research Foundation(Grant No.2023A1515030184).
文摘In recent years,GNSS-derived total electron content(TEC)measurements have emerged as an effective method for detecting natural hazards through their ionospheric manifestations.Seismo-atmospheric disturbances generated by earthquakes,tsunamis,and volcanic eruptions propagate as traveling ionospheric disturbances(TIDs)that modify ionospheric electron density.Despite this potential,specialized open-source tools for such analyses remain limited.We present IonKit-NH,a MATLAB-based toolkit enabling systematic processing of multi-GNSS data(GPS,GLONASS,Galileo,BDS)through dual-frequency combination analysis for TEC derivation.The software implements automated generation of time-distance diagrams and 2D TEC perturbation maps,enabling quantitative characterization of TID propagation parameters associated with natural hazards.This toolkit enhances standardized analysis of ionospheric precursors and co-seismic signals across global navigation satellite systems.
文摘电离层总电子含量(Total Electron Content,TEC)精确预报对提高卫星导航定位精度具有重要意义.为此,提出一种联合鲸鱼优化算法(Whale Optimization Algorithm,WOA)与长短期记忆神经网络模型(Long-Short Term Memory Networks,LSTM)的TEC短期预报模型;该模型通过LSTM模型训练得到WOA算法的最佳适应度,并利用优化的WOA算法得到LSTM模型最优参数.最后,结合欧洲定轨中心(Center for Orbit Determination in Europe,CODE)提供的TEC格网点数据对所提模型进行验证;试验结果表明:地磁平静状态下,组合模型的平均相关系数ρ较LSTM模型在低、中、高纬度分别提升了2.8%、6.2%和14.8%;地磁活跃状态下组合模型的平均相关系数ρ在低、中、高纬度地区较LSTM模型分别提升了6.6%、9.2%与7.9%.且模型预报效果与地磁活跃状态、季节、太阳活跃水平等有关,在不同地磁活跃状态、季节与不同太阳活动水平情况下,组合模型预报效果均优于单一LSTM模型,为电离层TEC预报模型的实际应用提供了参考.
