用Transformer模型提前3天预测电离层密度，提升低轨卫星轨道管理精度。
Forecasting Thermospheric Density with Transformers for Multi-Satellite Orbit Management
- 直接使用紧凑输入数据，无需复杂预处理和空间降维。
- 相比传统经验模型，预测精度显著提升，可支持3天前瞻规划。
- 适合需要高精度轨道预测的卫星任务与航天机构应用。
精确预测电离层密度对低地球轨道卫星的可靠运行至关重要，尤其在太阳和地磁活动高峰期。基于物理的模型如TIE-GCM具有高保真度但计算成本高，而经验模型如NRLMSIS虽高效却缺乏预测能力。本文提出一种基于Transformer的密度预测模型，可提前三天进行预报，旨在作为经验模型的即插即用替代方案。与近期方法不同，该模型避免了空间降维和复杂的输入管道，直接处理紧凑输入集。在真实数据上验证表明，其在关键预测指标上表现更优，展现出支持任务规划的潜力。
原文摘要 · Abstract (English)
Accurate thermospheric density prediction is crucial for reliable satellite operations in Low Earth Orbits, especially at high solar and geomagnetic activity. Physics-based models such as TIE-GCM offer high fidelity but are computationally expensive, while empirical models like NRLMSIS are efficient yet lack predictive power. This work presents a transformer-based model that forecasts densities up to three days ahead and is intended as a drop-in replacement for an empirical baseline. Unlike recent approaches, it avoids spatial reduction and complex input pipelines, operating directly on a compact input set. Validated on real-world data, the model improves key prediction metrics and shows potential to support mission planning.
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