Denoising Signal And Application Of MEMS Vector Hydrophone Based On Modal Decomposition

Vector hydrophone is a new developed form on the basis of scalar hydrophone,which is a better orientation and positioning than scalar hydrophone.Microelectromechanical system(MEMS)technology is applied to vector hydrophone is an attempt to innovate approach and innovate the principles.MEMS vector hydrophone has the advantages of vector,small size,good consistency and mass production et al.With the continuous development of science and technology,MEMS vector hydrophone types are increasingly numerous and its performance has gradually become mature.But it will still be mixed with noise when receiving signal data.In order to better target the next step orientation or imaging study,it is necessary to denoise the hydrophone array signal firstly.This paper systematically studies the denoising and application of different modal decomposition methods in MEMS vector hydrophone signals.The denoising effect and performance index of different modal decomposition are verified by using the simulation data and the Fenhe measured data of the National Defense Key Laboratoryof the North University of China.The main contents of this paper include:(1)The traditional signal denoising methods,such as Fourier transform method,adaptive denoising method and morphological filtering method et al,have some denoising effect in the weak sound of underwater sound,but there are some shortcomings.In this paper,the modal decomposition method is used to decompose the noisy signal,and then according to the denoising principle of modal decomposition method the decomposed signal is denoised.The denoising effect and performance index of the simulated signal are compared with that of a series of empirical mode decomposition methods,which is based on the denoising effect and performance index of the simulation experiment.(2)Due to the effect of modal aliasing caused by the decomposition of the noisy signal,the empirical mode decomposition results in the problem of signal distortion and denoising effect when selecting the intrinsic mode function.In this paper,the de-noising ability of the algorithm is improved by re-denoising intrinsic mode function.According to the basic knowledge of signal processing,random noise is basically in the high frequency part.The intrinsic mode function of the noisy signal is extracted and reused by wavelet threshold denoising and wavelet packet denoising to deal with the obvious modal function of the signal,which further improves the denoising effect and reduces the signal distortion.(3)Due to the complexity of the measured data,only the empirical mode decomposition method can not be used to remove the noise in the Fenji measured data.According to the spectrum analysis of the measured data,the measured data not only have high frequency random noise,but also low frequency drift interference.In this paper,the method of combining empirical mode decomposition with wavelet denoising is applied to the de-noising of the measured data,and the better recovery of the source cosine signal is obtained.Further,it can reduce the effect of modal aliasing.Finally,the variational mode decomposition algorithm is used to denoise the measured data,and the method is applied to solve the problem of modal aliasing.And it is superior to the empirical mode decomposition and wavelet combining method in the denoising effect.