基于GNSS数据的电离层四维多尺度层析成像技术研究

发布时间：2018-01-27 03:04

本文关键词： GNSS 多次反演电离层层析电子密度　出处：《中国民航大学》2017年硕士论文　论文类型：学位论文

【摘要】：基于全球导航卫星系统(Global Navigation Satellite System,GNSS)的电离层层析是一种全天候、大范围的电离层探测技术,具有费用低、操作简单、探测范围广等诸多优势。尤其是现在GNSS的快速发展,GPS、伽利略、GLONASS、北斗等卫星系统的出现,为电离层层析带来大量的可用数据源,同一时刻电离层中有更多卫星信号穿过,为获取更多精确输入数据值提供了技术保证,从而实现利用层析技术获得电离层电子密度的时空分布。目前的电离层层析技术特别适用于大尺度电离层结构重构及电离层扰动形态的监测,但对中小尺度电离层结构的重构则受到地面数据分布不均匀、投影不完全等局限,不能进行有效的图像反演。为了探测电离层多尺度(包括大尺度和中小尺度)结构的电子密度时空分布,本文利用一种多次反演(multi pass inversion,multi)的层析算法,获取美国西海岸密集的地基GPS观测数据、欧洲地区多星座GNSS数据,进行多分辨率的电离层层析成像,实现电离层多尺度结构探测。首先以北美地区上空电离层为主要研究对象,分析时间窗选取和经验正交函数(EOF)对multi反演算法结果的影响,选择最佳算法设置,进而借助和no-multi反演层析结果的对比,分析multi反演算法的特点和优越性。其次,针对不同的地面站数据选取,借助与电离层测高仪的峰值电子密度实测值的对比,讨论了地面观测数据的分布密度对multi反演层析结果的影响;最后,用该算法分析了北美地区以及欧洲地区地磁平静态和磁暴时期电离层多尺度结构时空变化,同时分析基于GNSS数据的层析结果与基于GPS数据的层析结果的区别。实验结果表明,在反演区域内,multi反演算法进行多分辨率的格网划分实现了电离层四维多尺度层析成像,并且在层析精度上比no-multi的反演算法精度更高。在未来的电离层模型设计中可以考虑使用该算法,从而实现检测电离层连续时空变化的目的,尤其是对电离层不同尺度扰动结构的变化,从而为民航应用卫星导航系统提供所需要的性能保证。
[Abstract]:Ionospheric tomography based on Global Navigation Satellite system Navigation is an all-weather condition. The wide range of ionospheric detection technology has many advantages, such as low cost, simple operation, wide range of detection and so on. Especially, the rapid development of GNSS now, Galileo GLONASS. The emergence of satellite systems such as Beidou brings a large number of available data sources for ionospheric tomography. At the same time, more satellite signals pass through the ionosphere, which provides a technical guarantee for obtaining more accurate input data. The current ionospheric tomography technique is especially suitable for large-scale ionospheric structural reconstruction and ionospheric disturbance morphology monitoring. However, the reconstruction of the mesoscale ionospheric structure is limited by the uneven distribution of ground data and incomplete projection. In order to detect the spatial and temporal distribution of electron density in the ionospheric multiscale (including large scale and small scale) structures, it is impossible to carry out effective image inversion. In this paper, a multi pass inversion multiplex tomography algorithm is used to obtain the dense ground GPS observation data on the west coast of the United States. The multi-constellation GNSS data in Europe are used for multi-resolution ionospheric tomography to detect the multi-scale structure of the ionosphere. Firstly, the ionosphere over North America is the main research object. The influence of time window selection and empirical orthogonal function (EOF) on the multi inversion algorithm is analyzed, and the optimal algorithm setting is selected, and then compared with the no-multi inversion tomography results. The characteristics and advantages of the multi inversion algorithm are analyzed. Secondly, according to the different ground station data selection, with the help of the ionospheric altimeter peak electron density measured by comparison. The influence of the distribution density of ground observation data on the multi inversion tomography results is discussed. Finally, the temporal and spatial variations of the ionospheric multiscale structure during the geomagnetic flat static and magnetic storm periods in North America and Europe are analyzed by the proposed algorithm. At the same time, the difference between the chromatographic results based on GNSS data and that based on GPS data is analyzed. The experimental results show that it is in the inversion region. The multi-resolution grid partition of multi inversion algorithm is used to realize the four-dimensional multi-scale tomography of the ionosphere. And the accuracy of tomography is higher than that of no-multi 's inversion algorithm, which can be used in the future ionospheric model design, so as to achieve the purpose of detecting continuous ionospheric temporal and spatial changes. In particular, the variation of the ionospheric disturbance structure at different scales provides the required performance guarantee for the civil aviation application satellite navigation system.
【学位授予单位】：中国民航大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN967.1;P352

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