一种通用型基于经验模态分解的小波阈值滤波方法研究

首先针对中高频水声信号，提出一种改进的经验模态分解加小波软阈值滤波方法；然后将信号进行带通滤波处理及经验模态分解，将分解得到的各个模态转换为频域信号，采用小波软阈值方法在频域上对这些模态进行滤波，最后对信号进行重构，并将其转换为时域信号。分别采用本方法和原时域上的小波阈值方法对不同频率的水声信号进行滤波，经计算分析可知，对频率小于800 Hz的水声信号，采用原方法可获得较好的滤波效果；当信号频率大于800 Hz时，采用本方法的滤波效果更好，因此应针对不同频率的水声信号，选择合适的滤波方法，以获得满意的滤波效果。

A universal wavelet soft threshold filtering me-thod based on improved empirical mode decomposition

This work studies a method for filtering intermediate and high frequency underwater acoustic signal based on the ensemble empirical mode decomposition (EEMD) and the wavelet soft threshold (WST) methods. Firstly, the band-pass filter is used to denoise the signal with noise. Secondly, the EEMD method is used to process the signal, then the intrinsic mode functions (IMFs) are transformed to signals in frequency domain, respectively. Thirdly, the IMFs in frequency domain are filtered by using the WST method. Finally, the IMFs are added to reconstruct the signal in frequency domain, and then the signal in time domain is obtained. This method and the original WST method are used to filter the underwater acoustic signal with different frequencies respectively. The following acquaintances can be observed: When the frequency of the underwater acoustic signal is less than 800Hz, the original filtering method can obtain better result. However, when the frequency is more than 800Hz, the new method can get better result. In order to obtain the satisfied filtering result, the filtering method should be chosen based on the frequency of the underwater acoustic signal.