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English(EN) Forecasting Ionospheric Irregularities on GNSS Lines of Sight Using Dynamic Graphs with Ephemeris Conditioning

新的动态图模型可高精度预测电离层不规则性

研究人员开发了一种使用动态图和星历条件约束来预测电离层不规则性的新方法。该方法将电离层建模为一个图,其中连通性随卫星位置而变化,从而可以预测未来可能出现的视线。在新加坡的全球导航卫星系统数据上进行测试,IonoDGNN模型在提前两小时预测TEC指数变化率不规则性方面取得了0.55的Brier技能得分和0.77的AUC。研究表明,动态图预测为传统的网格化电离层模型提供了一种可行的替代方案,尤其是在处理覆盖中断时。 AI

影响 引入了一种新颖的基于图的AI方法,用于改进电离层预测,可能提高GNSS精度。

排序理由 学术论文,详细介绍了一种用于科学预测任务的新方法和模型。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的动态图模型可高精度预测电离层不规则性

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学术论文,详细介绍了一种用于科学预测任务的新方法和模型。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Mert Can Turkmen, Eng Leong Tan, Yee Hui Lee ·

    使用动态图和星历条件预测GNSS视线上的电离层不规则性

    arXiv:2604.18379v3 Announce Type: replace Abstract: Most data-driven ionospheric models operate on gridded products, which do not preserve the time-varying sampling structure of satellite-based sensing. We instead model the ionosphere as a dynamic graph over ionospheric pierce po…