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使用机器学习增强实时全球电离层地图

Marcel Iten¹, Shuyin Mao¹, Yuanxin Pan¹

¹ETH Zurich, Zurich, Switzerland.

GPS solutions

|May 15, 2025

概括

机器学习改进了全球导航卫星系统 (GNSS) 的实时电离层地图,大大减少了错误. 这提高了高精度应用的准确性,并提高了单频GNSS定位性能.

科学领域:

地质物理学地质物理学
太空科学太空科学
卫星导航卫星导航卫星导航卫星导航

背景情况:

全球电离层地图 (GIM) 对于高精度的全球导航卫星系统 (GNSS) 应用至关重要.
由于延迟,国际GNSS服务 (IGS) 的实时GIM (RT GIM) 的准确性低于最终GIM.
与最终的GIM相比,当前的IGS RT GIM显示了3.5-5.5总电子含量单位 (TECU) 的根平均平方误差 (RMSE).

研究的目的:

通过机器学习 (ML) 提高现有的实时GIM的准确性.
调查卷积神经网络 (CNN) 和条件生成对抗网络 (cGAN) 对GIM改进的有效性.
评估ML增强的GIM对单频GNSS定位的影响.

主要方法:

将CNN和cGAN模型应用于IGS组合的RT GIM和加泰罗尼亚理工大学 (UPC) GIM.
利用了超过13万对实时和最终GIM用于培训和验证.
在3.5个月的测试期内评估性能.

主要成果:

实现了RT GIM中平均绝对误差的30%以上的降低.
对于具有高垂直总电子含量 (VTEC) 值的区域,准确度明显提高了近50%.
在单频GNSS用户的3D定位误差中观察到高达21厘米的减少.

结论:

机器学习方法,特别是CNN和cGAN,显示了提高实时GIM准确性的巨大潜力.
开发的方法为生成更准确和精细的实时电离层产品提供了可行的解决方案.
增强的RT GIM可以在实时GNSS应用中提高性能.

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