用动态图模型预测卫星信号路径的电离层不规则，提前2小时预报准确率显著提升。
Forecasting Ionospheric Irregularities on GNSS Lines of Sight Using Dynamic Graphs with Ephemeris Conditioning
- 构建随卫星位置变化的动态图，以信号路径点为节点建模电离层
- 在2小时内预测电离层不规则，BSS达0.55，较基准提升53%
- 适用于信号缺失场景，通过邻近节点信息恢复预测，适合高精度导航应用
现有数据驱动的电离层模型多基于网格化产品，无法保留卫星观测的时间动态采样结构。本文将电离层建模为动态图，以电离层穿刺点为节点，连接关系随卫星位置变化而演化。由于卫星轨道可预测，未来时间窗内的图结构可预先构建。我们利用此特性，以未来图结构对预测进行条件约束，称为星历条件化（ephemeris conditioning），实现仅在预报时段出现的信号路径上的预测。在2023至2025年新加坡双站GNSS数据上评估，任务为提前2小时对每条信号路径（LoS）的电离层不规则（以ROTI定义）进行二分类。所提模型IonoDGNN达到Brier Skill Score（BSS）0.55，PR-AUC 0.77，相较持续性基准分别提升53%和58%，长时预测优势更明显。消融实验表明图结构与星历条件化均贡献显著。在模拟信号覆盖中断下，模型仍能通过空间消息传递从已观测邻居恢复预测性能，优于插值基线，尤其在高丢失率下表现更优。结果表明，基于动态信号路径的图模型是电离层建模的有效替代方案。项目与数据集见https://github.com/Mert-chan/IonoDGNN。
原文摘要 · Abstract (English)
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 points, with connectivity that evolves as satellite positions change. Because satellite trajectories are predictable, the graph topology over the forecast horizon can be constructed in advance. We exploit this property to condition forecasts on the future graph structure, which we term ephemeris conditioning. This enables prediction on lines of sight (LoS) that appear only in the forecast horizon. We evaluate our framework on Global Navigation Satellite System data from a co-located receiver pair in Singapore spanning 2023 to 2025. The task is forecasting irregularities defined by the Rate of TEC Index (ROTI) up to 2 hours ahead as per-node binary classification. The resulting model, IonoDGNN, achieves a Brier Skill Score (BSS) of 0.55 and an area under the precision-recall curve (PR-AUC) of 0.77. These correspond to improvements over persistence of 53% in BSS and 58% in PR-AUC, with larger gains at longer lead times. Ablations confirm that graph structure and ephemeris conditioning each contribute meaningfully. Under simulated coverage dropout, the model retains predictive skill on affected nodes through spatial message passing from observed neighbors. Compared to interpolation baselines, the proposed model achieves better recovery, especially at higher dropout levels. These results suggest that dynamic graph forecasting on evolving LoS is a viable alternative for ionospheric modeling. The project and the dataset are available at https://github.com/Mert-chan/IonoDGNN.
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