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<p>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&#xa0;cm. Overall, our proposed method demonstrates great potential in generating more accurate and refined real-time GIMs.</p>

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Enhanced real-time global ionospheric maps using machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月12日

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  • Marcel Iten,
  • Shuyin Mao,
  • Yuanxin Pan,
  • Benedikt Soja

摘要

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.