电子测量与仪器学报

马星河,师雪琳,赵军营.基于POA-VMD-WT的MEMS去噪方法[J].电子测量与仪器学报,2024,38(1):53-63

基于POA-VMD-WT的MEMS去噪方法

Denoising method for MEMS sensor signal based on POA-VMD-WT

DOI：

英文关键词:MEMS sensor pelican optimization algorithm (POA) variational mode decomposition (VMD) wavelet threshold (WT) cosine similarity index

基金项目:河南省重点科技攻关项目（182102310936）资助

作者	单位
马星河	河南理工大学电气学院河南焦作454000
师雪琳	河南理工大学电气学院河南焦作454000
赵军营	河南理工大学电气学院河南焦作454000

Author	Institution
Ma Xinghe	School of Electrical Engineering, Henan Polytechnic University, Jiaozuo 454000,China
Shi Xuelin	School of Electrical Engineering, Henan Polytechnic University, Jiaozuo 454000,China
Zhao Junying	School of Electrical Engineering, Henan Polytechnic University, Jiaozuo 454000,China

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中文摘要:

针对MEMS传感器所测得的加速度和角速度输出信号噪声较大问题,提出一种基于鹈鹕优化算法(pelican optimization algorithm,POA)的变分模态分解(variational mode decomposition,VMD)结合小波阈值(wavelet threshold,WT)的去噪方法。首先利用POA对VMD的参数组合进行优化选择,然后应用POA-VMD将含噪信号自适应、非递归地分解为一系列本征模态函数（intrinsic mode function,IMF)。再通过计算每个IMF的余弦相似度对IMFs进行分类，根据计算结果将IMFs分为噪声主导分量与信号主导分量,对分类后的噪声主导分量进行改进小波阈值去噪处理,最后对处理后的噪声分量与信号主导分量进行重构,获得降噪后的MEMS传感器信号。静态和动态实验结果表明,该方法去噪处理后信号的信噪比分别提高12和10 dB,均方误差分别降低75.5%和46.6%,去噪效果显著,能够提高MEMS传感器的精度。

英文摘要:

To address the issue of significant noise present in the acceleration and angular velocity output signals measured by MEMS sensors, a denoising method based on the pelican optimization algorithm (POA) of variational mode decomposition (VMD) and wavelet thresholding (WT) is proposed. Firstly, POA is used to optimally select the parameter combination of the VMD, then POA-VMD is applied to adaptively and non-recursively decompose the noisy signal into a series of intrinsic modal functions (IMF). Secondly, the IMFs are classified by calculating the cosine similarity of each IMF. Based on the result of the calculation, IMFs are classified into noise-dominant component and signal-dominated component. After classification, the noise-dominated component is subjected to improved wavelet threshold denoising, and finally the processed noise-dominated component is reconstructed with the signal-dominated component to obtain the noise-reduced MEMS sensor signal. The static and dynamic experimental results show that the signal-;to-noise ratio of the denoised signal is improved by 12 and 10 dB respectively, and the mean square error is reduced by 75.5% and 46.6% respectively, which is a significant denoising effect and can improve the accuracy of the MEMS sensor.

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