一种深度学习方法来消除卫星磁场测量中的多源短暂干扰
Ning Li1,2, Jindong Wang1, Shanzhi Ye1
1State Key Laboratory of Solar Activity and Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
一种名为多源自适应梯度计 (MSAG) 的新方法使用人工智能准确纠正航天器上的磁场干扰. 这种先进的技术提高了太空科学任务的数据质量,大大减少了错误.
科学领域:
- 太空科学 太空科学
- 天体物理学 天体物理学
- 地质物理学 地质物理学
背景情况:
- 航天器磁场测量非常重要,但容易受到船上系统的干扰.
- 现有的方法,如磁性清洁和长杆,在成本和复杂性方面都有局限性.
研究的目的:
- 引入和验证多源自适应梯度计 (MSAG) 进行精确的磁场测量.
- 为了证明MSAG能够在没有手动数据分割的情况下纠正多源干扰的能力.
主要方法:
- 开发了一种增强的梯度计技术,整合了一个用于干扰分类的神经网络.
- 训练网络识别干扰类型并应用适应性校正系数.
- 使用基于真实卫星测量和蒙特卡洛模拟的合成数据验证的MSAG.
主要成果:
- MSAG显著降低了中位数根平均平方误差 (RMSE) 从0.457 nT降至0.014 nT.
- 实现了0.999994的高中位相关系数与地面真相.
- 证明有效的多源干扰纠正.
结论:
- 在太空任务中,MSAG为磁场恢复提供了比传统方法更好的替代方案.
- 这种技术可能会放松对磁性清洁和杆长度的约束.
- 突出了机器学习和梯度计的协同作用,用于高准确度的基于空间的磁场测量.
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