Signal Denoising Based on Wavelet Threshold Denoising and Optimized Variational Mode Decomposition
Signal Denoising Based on Wavelet Threshold Denoising and Optimized Variational Mode Decomposition
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基于小波阈值去噪和优化变分模态分解的信号去噪
DOI：
10.1155/2021/5599096
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发表时间：
2021-07-14
影响因子：
1.9
通讯作者：
Shi, Na
中科院分区：
文献类型：
--
作者：
Hu, Hongping;Ao, Yan;Shi, Na
为了消除MEMS矢量水听器接收信号中的噪声，提出了一种基于小波阈值去噪、多元优化（MVO）和粒子群优化（PSO）混合算法优化的变分模式分解（VMD）和相关系数（CC）判断的MEMS矢量水听器信号去噪联合算法（MVO-PSO-VMD-CC-WT），其适应度函数为均方根误差（RMSE），个体为VMD参数。对每个个体，利用VMD将原始信号分解为纯分量、含噪分量和噪声分量，根据CC判断，直接保留纯分量，对含噪分量进行小波去噪，丢弃噪声分量，然后将去噪后的含噪分量和纯分量重构为原始信号的去噪信号。然后，利用获得的最优个体，通过使用上述重复的信号处理的MVO-PSO-VMD-CC-WT进行信号去噪。两个仿真实验结果表明，MVO-PSO-VMD-CC-WT算法具有最高的信噪比和最小的均方根误差，优于其他比较算法的上级。并将所提出的MVO-PSO-VMD-CC-WT算法有效地应用于实际湖试信号的去噪。因此，本文提出的MVO-PSO-VMD-CC-WT算法适用于信号去噪，可应用于实际的信号处理实验中。
To eliminate the noise from the signals received by MEMS vector hydrophone, a joint algorithm is proposed in this paper based on wavelet threshold (WT) denoising, variational mode decomposition (VMD) optimized by a hybrid algorithm of Multiverse Optimizer (MVO) and Particle Swarm Optimization (PSO), and correlation coefficient (CC) judgment to perform the signal denoising of MEMS vector hydrophone, named as MVO-PSO-VMD-CC-WT, whose fitness function is the root mean square error (RMSE) and whose individual is the parameters of VMD. For every individual, the original signal is decomposed by VMD into pure components, noisy components, and noise components in terms of CC judgment, where the pure components are directly retained, the noisy components are denoised by WT denoising, and the noise components are discarded, and then, the denoised noisy components and the pure components are reconstructed to be the denoised signal of the original signal. Then, the obtained optimal individual is utilized to perform the signal denoising by MVO-PSO-VMD-CC-WT by the use of the above repeated signal processing. Two simulated experimental results show that the MVO-PSO-VMD-CC-WT algorithm which has the highest signal-to-noise ratio and the least RMSE is superior to the other compared algorithms. And the proposed MVO-PSO-VMD-CC-WT algorithm is effectively applied to perform the signal denoising of the actual lake experiments. Therefore, the proposed MVO-PSO-VMD-CC-WT is suitable for the signal denoising and can be applied into the actual experiments in signal processing.