Signal Denoising Method Based on EEMD and SSA Processing for MEMS Vector Hydrophones.

IF 3 3区 工程技术 Q2 CHEMISTRY, ANALYTICAL
Micromachines Pub Date : 2024-09-24 DOI:10.3390/mi15101183
Peng Wang, Jie Dong, Lifu Wang, Shuhui Qiao
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引用次数: 0

Abstract

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.

基于 EEMD 和 SSA 处理的 MEMS 矢量水听器信号去噪方法
矢量水听器在水下声学工程中的作用越来越突出,是许多国家的研究热点,但它也存在一些不足。对于微机电系统(MEMS)矢量水听器接收信号的混合问题,在存在大量外部环境噪声的情况下,不可避免地会出现噪声和漂移。这种失真现象给进一步的信号检测和识别带来了困难。本研究提出了一种结合集合经验模式分解(EEMD)和奇异频谱分析(SSA)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,使用傅立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。每个 IMF 分量在中心的频率进一步确定该 IMF 分量属于噪声 IMF 分量、无效 IMF 分量还是纯 IMF 分量。然后,保留纯 IMF 分量,去除噪声 IMF 分量和无效 IMF 分量。最后,脱盐后的 IMF 通过 SSA 重构信号,得到去噪信号,实现对信号的去噪处理,提取有用信号,去除漂移。SSA 的作用是有效分离趋势噪声和周期振动噪声。与分别采用 EEMD 和 SSA 算法相比,所提出的 EEMD-SSA 算法具有更好的去噪效果,并能实现漂移的去除。随后,EEMD-SSA 被用于处理汾河测量的数据。实验由北大进行。仿真和湖泊测试结果表明,所提出的 EEMD-SSA 算法具有一定的实用研究价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Micromachines
Micromachines NANOSCIENCE & NANOTECHNOLOGY-INSTRUMENTS & INSTRUMENTATION
CiteScore
5.20
自引率
14.70%
发文量
1862
审稿时长
16.31 days
期刊介绍: Micromachines (ISSN 2072-666X) is an international, peer-reviewed open access journal which provides an advanced forum for studies related to micro-scaled machines and micromachinery. It publishes reviews, regular research papers and short communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
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