摘要
针对MEMS矢量水听器的噪声去除问题,将集合经验模态分解(EEMD)、小波阈值去噪(WT)和奇异谱分析(SSA)相结合,提出了一种联合EEMD-WT-SSA去噪算法.该算法首先将含噪信号分解为一系列固有模态函数(IMF),然后,用连续均方误差准则(CMSE)对高频和低频进行区分,对高频信号进行小波阈值去噪,再和低频信号进行重构,最后对重构信号利用奇异值分析方法进行恢复,得到目标信号.通过仿真实验和湖试实验的信号处理表明,所提算法在信噪比和均方误差两个评价指标方面,相对于EEMD和EEMD-WT算法,具有明显的优势.
Aiming at the problem of MEMS vector hydrophone denoising,a new combined EEMD-WT-SSA algorithm is proposed by combining the combination of set empirical mode decomposition(EEMD),wavelet threshold denoising(WT)and singular spectrum analysis(SSA).Firstly,the noise-containing signal is decomposed into a series of intrinsic mode functions(IMF),then the high frequency and low frequency are distinguished by continuous mean square error criterion(CMSE),the threshold value of high frequency signal is de-noising,and then the low-frequency signal is reconstructed.Finally,the reconstruction signal is recovered by singular value analysis method to obtain the target signal.The signal processing of simulation and lake experiment shows that the proposed algorithm has obvious advantages over EEMD and EEMD-WT in the aspect of SNR and MSE.
作者
郭帆
王鹏
GUO Fan;WANG Peng(College of Science, North University of China, Taiyuan 030051, China)
出处
《测试技术学报》
2022年第2期117-121,177,共6页
Journal of Test and Measurement Technology
基金
国家自然科学基金资助项目(61774137)
山西省重点研发计划资助项目(201903D121156)
山西省回国留学人员科研资助项目(2020-104,2021-108)。
关键词
集合经验模态分解
小波阈值算法
奇异谱分析
去噪算法
ensemble empirical mode decomposition
wavelet threshold denoising
singular spectrum analysis
denosing algorithm
