基于Noise2Noise方法的引力参考传感器或加速度计的无监督降噪
Zhilan Yang1,2,3, Haoyue Zhang4, Peng Xu3,4,5
1National Space Science Center, Chinese Academy of Sciences, Beijing 100094, China.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
深度学习有效地抑制空间任务的静电悬浮惯性传感器中的噪音. 这种先进的方法优于传统技术,提高了引力和引力波检测的信号质量.
科学领域:
- 太空物理空间物理学
- 传感器技术 传感器技术
- 信号处理 信号处理
背景情况:
- 机载静电悬浮惯性传感器对于重力卫星和引力波检测至关重要.
- 复杂的空间环境创造了复杂的噪声模式,限制了传统的噪声建模和减法方法.
- 提高高精度惯性传感器的信号噪声比对于任务的成功至关重要.
研究的目的:
- 应用深度学习技术来提高车载静电悬浮惯性传感器的信号质量.
- 解决复杂空间环境中传统降噪方法的局限性.
- 开发一种新的深度学习方法,用于在基于卫星的惯性传感中抑制噪音.
主要方法:
- 为一般和周期信号设计的奇偶和周期子采样器.
- 集成的重建层与完全连接的网络进入深度学习模型.
- 使用模拟数据,GRACE-FO和Taiji-1加速数据验证了模型.
主要成果:
- 与传统的数据平滑相比,深度学习方法表现出更高的性能.
- 拟议的方法有效地抑制了惯性传感器测量中的噪声.
- 实验结果证实了深度学习模型在真实世界卫星数据上的有效性.
结论:
- 深度学习为太空应用的高精度惯性传感器提供了一种强大的降噪解决方案.
- 开发的部分采样和重建技术在具有挑战性的轨道条件下改善了信号噪声比.
- 这项工作推进了深度学习在太空探索和基本物理任务中的潜力.
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