| 刘 迪,杨志强,王德娟,李 航.磁悬浮陀螺转子信号的局部均值分解降噪研究[J].电子测量与仪器学报,2022,36(1):20-29 |
| 磁悬浮陀螺转子信号的局部均值分解降噪研究 |
| Research on noise reduction of rotor signal of maglev gyroscopebased on local mean decomposition |
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| DOI： |
| 中文关键词: 磁悬浮陀螺仪 转子电流信号 局部均值分解 信号降噪 |
| 英文关键词:maglev gyroscope rotor current signal local mean decomposition signal noise reduction |
| 基金项目:国家自然科学基金（42174054）、长安大学中央高校基本科研业务费专项资金（300102260206）项目资助 |
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| 中文摘要: |
| 磁悬浮陀螺转子电流信号对环境变化高度敏感,信号采样过程中不可避免会引入噪声,针对该问题提出一种基于局部
均值分解(local mean decomposition,LMD),融合豪斯多夫(Hausdorff)距离与阈值降噪( threshold denoising,TD)的算法以减弱噪
声干扰。 首先对原始信号进行局部均值分解,得到若干乘积函数(PF)分量和一个余量,然后根据各 PF 分量与原始信号间的豪
斯多夫距离判定噪声、信号分量,再对噪声分量进行阈值处理,最后将阈值处理后的噪声分量、信号分量及余量进行叠加得到重
构信号,实现陀螺仪转子电流信号的降噪。 仿真实验结果表明,重构信号的信噪比相对于原始信号平均提高了 12. 86 db,均方
根误差平均降低了 9. 25×10
-6 A;实测信号降噪结果表明,该降噪算法对四条导线边的滤波增益分别为 40. 0%、93. 5%、30. 8%和
50. 0%。 |
| 英文摘要: |
| The rotor current signal of maglev gyro is highly sensitive to environmental changes, therefore, noise will inevitably be
introduced in the process of signal sampling. To solve this problem, an algorithm based on local mean decomposition (LMD) and fusion
of Hausdorff distance and threshold denoising (TD) is proposed to reduce noise interference. Firstly, the original signal is decomposed
into several PF components and a margin. Then, the noise and signal components are determined according to the Hausdorff distance
between each pf component and the original signal. Then, the noise component is denoised by threshold. Finally, the noise components,
signal components and margin after thresholding are superimposed to obtain the reconstructed signal, so as to realize the reconstruction of
the gyroscope rotor current signal Noise reduction. The simulation results show that the signal-to-noise ratio of the reconstructed signal is
12. 86 db higher than that of the original signal, and the root mean square error is 9. 25×10
-6 A lower than that of the original signal.
The de-noising results of measured signals show that the filtering gains of the de-noising algorithm for four wire sides are 40. 0%,
93. 5%, 30. 8% and 50. 0% respectively. |
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