高G MEMS 加速度计校准 基于EMD和时间频率峰值过的Denoising方法
Chenguang Wang1,2, Yuchen Cui2,3, Yang Liu4
1School of Information and Communication Engineering, North University of China, Taiyuan 030051, China.
Micromachines
|May 27, 2023
概括
本研究引入了一种使用实证模式分解 (EMD) 和时间频率峰值过 (TFPF) 的新型加速度计消噪方法. 综合方法有效地抑制校准噪声,同时保持信号完整性.
科学领域:
- 仪器仪表和测量仪器的使用
- 信号处理 信号处理
背景情况:
- 加速度计校准易受噪声的影响,影响数据的准确性.
- 现有的无声化方法可能会损害信号特性.
研究的目的:
- 提出和验证一种新的加速度计消噪方法.
- 在加速度计校准过程中有效抑制噪音.
- 为了在无声化后保留原始信号特征.
主要方法:
- 经验模式分解 (EMD) 将信号分解为内在模式函数 (IMF).
- 时间频率峰值过 (TFPF) 适用于中频IMF.
- 选择性地删除高频国际货币基金组织,并保留低频国际货币基金组织.
- 用于性能评估的信号重建和艾伦方差分析.
主要成果:
- 拟议的EMD + TFPF方法在校准过程中显著抑制随机噪声.
- 信号重建错误控制在0.5%以内.
- 过效应很大,与原始数据相比改善了97.4%.
结论:
- 在EMD+TFPF算法提供了一个有效的解决方案,加速仪的无声化.
- 这种方法成功地消除了噪声,同时保留了基本的信号特征.
- 该方法表现出优越的性能,与其他方法相比,通过艾伦差异进行评估.
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