Wang Peng, Dong Jie, Wang Lifu, Qiao Shuhui
School of Mathematics, North University of China, Taiyuan 030051, China.
Micromachines (Basel). 2024 Sep 24;15(10):1183. doi: 10.3390/mi15101183.
The vector hydrophone is playing a more and more prominent role in underwater acoustic engineering, and it is a research hotspot in many countries; however, it also has some shortcomings. For the mixed problem involving received signals in micro-electromechanical system (MEMS) vector hydrophones in the presence of a large amount of external environment noise, noise and drift inevitably occur. The distortion phenomenon makes further signal detection and recognition difficult. In this study, a new method for denoising MEMS vector hydrophones by combining ensemble empirical mode decomposition (EEMD) and singular spectrum analysis (SSA) is proposed to improve the utilization of received signals. First, the main frequency of the noise signal is transformed using a Fourier transform. Then, the noise signal is decomposed by EEMD to obtain the intrinsic mode function (IMF) component. The frequency of each IMF component in the center further determines that the IMF component belongs to the noise IMF component, invalid IMF component, or pure IMF component. Then, there are pure IMF reserved components, removing noisy IMF components and invalid IMF components. Finally, the desalinated IMF reconstructs the signal through SSA to obtain the denoised signal, which realizes the denoising processing of the signal, extracting the useful signal and removing the drift. The role of SSA is to effectively separate the trend noise and the periodic vibration noise. Compared to EEMD and SSA separately, the proposed EEMD-SSA algorithm has a better denoising effect and can achieve the removal of drift. Following that, EEMD-SSA is used to process the data measured by Fenhe. The experiment is carried out by the North University of China. The simulation and lake test results show that the proposed EEMD-SSA has certain practical research value.
矢量水听器在水声工程中发挥着越来越突出的作用，是许多国家的研究热点；然而，它也存在一些缺点。对于微机电系统（MEMS）矢量水听器中存在大量外部环境噪声时接收信号的混合问题，不可避免地会出现噪声和漂移。这种失真现象使得进一步的信号检测和识别变得困难。在本研究中，提出了一种将总体经验模态分解（EEMD）和奇异谱分析（SSA）相结合的MEMS矢量水听器去噪新方法，以提高接收信号的利用率。首先，利用傅里叶变换对噪声信号的主频进行变换。然后，通过EEMD对噪声信号进行分解，得到本征模态函数（IMF）分量。进一步确定中心处各IMF分量的频率，判断该IMF分量属于噪声IMF分量、无效IMF分量还是纯IMF分量。接着，保留纯IMF分量，去除噪声IMF分量和无效IMF分量。最后，经过去噪的IMF通过SSA对信号进行重构，得到去噪后的信号，实现了信号的去噪处理，提取了有用信号并去除了漂移。SSA的作用是有效分离趋势噪声和周期性振动噪声。与单独使用EEMD和SSA相比，所提出的EEMD - SSA算法具有更好的去噪效果，并且能够实现漂移的去除。随后，利用EEMD - SSA对汾河实测数据进行处理。该实验由中北大学进行。仿真和湖试结果表明，所提出的EEMD - SSA具有一定的实际研究价值。
