Enhanced real-time global ionospheric maps using machine learning.

IF 4.5 1区 地球科学 Q1 REMOTE SENSING
GPS Solutions Pub Date : 2025-01-01 Epub Date: 2025-05-12 DOI:10.1007/s10291-025-01858-0
Marcel Iten, Shuyin Mao, Yuanxin Pan, Benedikt Soja
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引用次数: 0

Abstract

Global ionospheric maps (GIM) are commonly used ionospheric products in high-precision Global Navigation Satellite System (GNSS) applications. To meet the increasing demand for real-time (RT) applications, the International GNSS Service (IGS) officially started a real-time service in 2013. One of the tasks of the real-time service is the calculation of real-time GIMs. However, the accuracy of current real-time GIMs is still significantly worse than that of the final GIMs, which are the most accurate ionospheric products but have a latency of several days. The IGS RT GIMs exhibit an RMSE of around 3.5-5.5 total electron content units (TECU) compared to the final GIMs. This study focuses on improving the accuracy of existing real-time GIMs through machine learning (ML) approaches, specifically convolutional neural networks (CNN) and conditional generative adversarial networks (cGAN). We apply our method to the IGS combined real-time GIMs and to Universitat Politècnica de Catalunya (UPC) GIMs. We consider over 130'000 pairs of real-time and final GIMs. Over a 3.5-month test period, the proposed approach shows promising results with a reduction of more than 30% in mean absolute error for the real-time GIMs. Especially for regions with high VTEC values, we find a significant improvement of nearly 50%. The ML-enhanced real-time GIMs also exhibit improved positioning performance for single-frequency GNSS positioning with reductions in the 3D error up to 21 cm. Overall, our proposed method demonstrates great potential in generating more accurate and refined real-time GIMs.

利用机器学习增强实时全球电离层地图。
全球电离层地图(GIM)是高精度全球卫星导航系统(GNSS)应用中常用的电离层产品。为满足日益增长的实时应用需求,国际GNSS服务(IGS)于2013年正式启动了实时服务。实时业务的任务之一是实时GIMs的计算。然而,当前实时GIMs的精度仍然明显低于最终GIMs,后者是最精确的电离层产品,但具有数天的延迟。与最终的GIMs相比,IGS RT GIMs的RMSE约为3.5-5.5总电子含量单位(TECU)。本研究的重点是通过机器学习(ML)方法,特别是卷积神经网络(CNN)和条件生成对抗网络(cGAN),提高现有实时GIMs的准确性。我们将我们的方法应用于IGS联合实时GIMs和加泰罗尼亚政治大学(UPC)的GIMs。我们考虑了超过13万对实时和最终的GIMs。经过3.5个月的测试,该方法显示出令人满意的结果,实时GIMs的平均绝对误差降低了30%以上。特别是在VTEC值高的地区,我们发现有近50%的显著改善。ml增强的实时GIMs在单频GNSS定位中也表现出更好的定位性能,3D误差降低了21厘米。总的来说,我们提出的方法在生成更精确、更精细的实时GIMs方面显示出巨大的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
GPS Solutions
GPS Solutions 工程技术-遥感
CiteScore
8.10
自引率
12.20%
发文量
138
审稿时长
3.1 months
期刊介绍: GPS Solutions is a scientific journal. It is published quarterly and features system design issues and a full range of current and emerging applications of global navigation satellite systems (GNSS) such as GPS, GLONASS, Galileo, BeiDou, local systems, and augmentations. Novel, innovative, or highly demanding uses are of prime interest. Areas of application include: aviation, surveying and mapping, forestry and agriculture, maritime and waterway navigation, public transportation, time and frequency comparisons and dissemination, space and satellite operations, law enforcement and public safety, communications, meteorology and atmospheric science, geosciences, monitoring global change, technology and engineering, GIS, geodesy, and others. GPS Solutions addresses the latest developments in GNSS infrastructure, mathematical modeling, algorithmic developments and data analysis, user hardware, and general issues that impact the user community. Contributions from the entire spectrum of GNSS professionals are represented, including university researchers, scientists from government laboratories, receiver industry and other commercial developers, public officials, and business leaders.
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