Signal Denoising of MEMS Vector Hydrophone Based on Optimized VMD, Compressed Sensing, and Wavelet Threshold

Abstract

With the noise in underwater acoustic signal extracted from ocean background, the denoising algorithm based on the Variational Mode Decomposition (VMD) optimized by improved Grasshopper Optimization Algorithm (IGOA), the compressed sensing (CS) and wavelet threshold (WT) is proposed in this paper, named by IGOA-VMD-CS-WT, where VMD optimized by IGOA is utilized to perform sign composition and the obtained Intrinsic Mode Functions (IMF) are divided into effective components and noise components using cross-correlation coefficient of each IMF. CS is performed on sparse representation of noise components and the obtained sparse coefficients are processed with WT for the filters. The effective components and the denoised components are reconstructed to the denoised signal by the Orthogonal Matching Pursuit. The experiments show that IGOA-VMD-CS-WT has the highest signal-to-noise ratios and the least root mean square errors under different noise levels and has the better denoising effect on the denoising of the actual data.