基于EMD-SSA-VMD双层分解的陀螺仪去除算法
Chuanqian Lv1,2,3, Yaohong Zhao1,2, Fangzhou Li1,2,3
1Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China.
一种新的双层噪声抑制方法,EMD-SSA-VMD,有效地减少错误并提高MEMS陀螺仪的精度. 这种先进的技术提高了可靠的工程应用的信号质量.
科学领域:
- 工程 工程师 工程师 工程师
- 信号处理 信号处理
- 数据科学数据科学数据科学
背景情况:
- MEMS陀螺仪容易发生随机错误,影响测量精度.
- 有效的噪声抑制对于在各种应用中可靠的陀螺仪性能至关重要.
研究的目的:
- 为MEMS陀螺仪开发和验证一种新的双层噪声抑制方法.
- 改善信号噪声比 (SNR) 并减少陀螺仪测量中的根平均平方误差 (RMSE).
主要方法:
- 该EMD-SSA-VMD方法结合了实证模式分解 (EMD),搜索算法 (SSA) 和变化模式分解 (VMD).
- EMD将信号分解为内在模式函数 (IMFs);用于信号分类和噪声识别的理论 (PSE和SE).
- SSA优化了VMD参数,以增强信号分解和消除噪音.
主要成果:
- 与传统方法相比,MATLAB模拟显示了SNR和RMSE的显著改善.
- 在MEMS陀螺仪数据上的实验验证证证了算法的降噪效果.
- 无效的信号波形与原始信号非常相匹配,表明了高保真度.
结论:
- EMD-SSA-VMD算法为MEMS陀螺仪的噪声抑制提供了一个强大的和实用的解决方案.
- 这种方法提高了测量精度和可靠性,对工程应用非常有价值.
- 双层方法提供了卓越的降噪能力.
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