Signal Denoising Based on Wavelet Threshold Denoising and Optimized Variational Mode Decomposition

Hongping Hu,Yanping Bai,Yan Ao,Na Shi,Huichao Yan,Giovanni Diraco

doi:10.1155/2021/5599096

Abstract

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.

Highlights

There are rich resources in the marine environment, which are an important treasure trove to have influences on human development in the future [1, 2]
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) [21] denoising, variational mode decomposition (VMD) optimized by a hybrid algorithm of Multiverse Optimizer (MVO) [32] and Particle Swarm Optimization (PSO) [33], and correlation coefficient (CC) [34] 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
Two simulated experiments are given to verify the validation of MVOPSO-VMD-CC-WT by comparison with VMD-CC-WT, PSO-VMD-CC-WT, MVO-VMD-CC-WT, and MVOPSO-power spectral entropy (PSE)-VMD-CC-WT

Summary

Introduction

There are rich resources in the marine environment, which are an important treasure trove to have influences on human development in the future [1, 2]. More and more people have devoted to detecting the ocean and continually seeking the acoustic wave detection technology with long propagation distance, fast propagation speed, and small energy loss. A MEMS hydrophone [3] is an important tool to be applied to receive the underwater signal. The state parameters such as the target category, the relative angle, and the position of the sound source are obtained by processing the received signal. There exists the complex environment in the ocean, which leads to the complex acoustic wave. The received signals from the MEMS hydrophone are that the target signals are inevitably mixed with different noises, such as biological noise, background noise, and tugboat noise. It is necessary to denoise the signal to better understand the target signal and to apply the signal in a wider range, such as signal positioning, fault diagnosis, and analysis [4]

Results

Discussion

Conclusion