用机器学习预测电离层信号闪烁强度，提升导航可靠性。
Ionospheric Scintillation Forecasting Using Machine Learning
- 基于历史GNSS数据训练XGBoost模型预测闪烁等级。
- 模型在平衡数据集上达到77%预测准确率。
- 适合卫星导航、空间天气研究者参考。
本研究利用全球导航卫星系统（GNSS）闪烁监测接收机的历史数据，预测电离层电子密度不规则导致的信号幅度闪烁严重程度。这种闪烁可通过S4指数测量，但实时数据常不可得。研究构建机器学习模型，基于时间与空间因素预测闪烁强度，分为低、中、高三个等级。在六种模型中，XGBoost表现最优，在平衡数据集上实现77%的预测准确率。结果表明，机器学习能有效提升GNSS信号与导航系统的可靠性与性能。
原文摘要 · Abstract (English)
This study explores the use of historical data from Global Navigation Satellite System (GNSS) scintillation monitoring receivers to predict the severity of amplitude scintillation, a phenomenon where electron density irregularities in the ionosphere cause fluctuations in GNSS signal power. These fluctuations can be measured using the S4 index, but real-time data is not always available. The research focuses on developing a machine learning (ML) model that can forecast the intensity of amplitude scintillation, categorizing it into low, medium, or high severity levels based on various time and space-related factors. Among six different ML models tested, the XGBoost model emerged as the most effective, demonstrating a remarkable 77% prediction accuracy when trained with a balanced dataset. This work underscores the effectiveness of machine learning in enhancing the reliability and performance of GNSS signals and navigation systems by accurately predicting amplitude scintillation severity.
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