MEMS矢量水听器的一种新的联合去噪算法

A New Joint Denoising Algorithm for MEMS Vector Hydrophone

针对传感器水声信号存在随机噪声的问题,提出了一种正余弦算法(SCA)和粒子群算法(PSO)相结合优化变分模态分解(VMD)参数k和α,将含噪信号通过VMD分解为k个固有模态函数,选取相关系数高的模态分量进行小波阈值(WT)去噪后重构信号分量,得到目标信号的算法,记为SCA-PSO-VMD-WT算法.通过将本算法与VMD-WT,PSO-VMD-WT,SCA-VMD-WT算法相比,并从信噪比、均方误差2个评估指标发现本算法的去噪效果最好.

Aiming at the problem of random noise in sensor underwater acoustic signals,this paper proposes a method of combining sine cosine algorithm(SCA)and particle swarm optimization(PSO)to optimize the parameter of variational modal decomposition(VMD).The noisy signal is decomposed into an inherent modal function by VMD,and the modal component with high correlation coefficient is selected for wavelet threshold(WT)denoising and then the signal component is reconstructed to obtain the target signal algorithm,which is called SCA-PSO-VMD-WT algorithm.By comparing this algorithm with VMD-WT,PSO-VMD-WT,SCA-VMD-WT,and from two evaluation indexes of signal-to-noise ratio and mean square error,it is found that this algorithm has the best denoising effect.