Li Yuxing, Li Yaan, Chen Xiao, Yu Jing, Yang Hong, Wang Long
School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China.
School of Electronic and Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China.
Entropy (Basel). 2018 Jul 28;20(8):563. doi: 10.3390/e20080563.
Owing to the complexity of the ocean background noise, underwater acoustic signal denoising is one of the hotspot problems in the field of underwater acoustic signal processing. In this paper, we propose a new technique for underwater acoustic signal denoising based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), mutual information (MI), permutation entropy (PE), and wavelet threshold denoising. CEEMDAN is an improved algorithm of empirical mode decomposition (EMD) and ensemble EMD (EEMD). First, CEEMDAN is employed to decompose noisy signals into many intrinsic mode functions (IMFs). IMFs can be divided into three parts: noise IMFs, noise-dominant IMFs, and real IMFs. Then, the noise IMFs can be identified on the basis of MIs of adjacent IMFs; the other two parts of IMFs can be distinguished based on the values of PE. Finally, noise IMFs were removed, and wavelet threshold denoising is applied to noise-dominant IMFs; we can obtain the final denoised signal by combining real IMFs and denoised noise-dominant IMFs. Simulation experiments were conducted by using simulated data, chaotic signals, and real underwater acoustic signals; the proposed denoising technique performs better than other existing denoising techniques, which is beneficial to the feature extraction of underwater acoustic signal.
由于海洋背景噪声的复杂性，水下声学信号去噪是水下声学信号处理领域的热点问题之一。在本文中，我们提出了一种基于自适应噪声的完备总体经验模态分解（CEEMDAN）、互信息（MI）、排列熵（PE）和小波阈值去噪的水下声学信号去噪新技术。CEEMDAN是经验模态分解（EMD）和总体EMD（EEMD）的一种改进算法。首先，利用CEEMDAN将含噪信号分解为多个固有模态函数（IMF）。IMF可分为三部分：噪声IMF、噪声主导的IMF和真实IMF。然后，基于相邻IMF的互信息识别噪声IMF；基于PE值区分IMF的另外两部分。最后，去除噪声IMF，并对噪声主导的IMF应用小波阈值去噪；通过组合真实IMF和去噪后的噪声主导IMF，可得到最终的去噪信号。利用模拟数据、混沌信号和真实水下声学信号进行了仿真实验；所提出的去噪技术比其他现有去噪技术表现更好，这有利于水下声学信号的特征提取。
