文章摘要
丁浩,赵建昕,笪良龙.利用经验模态分解的高频水声信号滤波方法*[J].,2016,35(4):316-323
利用经验模态分解的高频水声信号滤波方法*
A filtering method for high frequency underwater acoustic signal using a improved empirical mode decomposition*
投稿时间:2015-08-30  修订日期:2016-06-14
中文摘要:
      研究了一种高频水声信号的滤波问题,提出了一种改进的经验模态分解加小波软阈值滤波方法。首先对信号进行带通滤波处理,再进行经验模态分解,将分解得到的各个模态转换为频域信号,选取信号主体所在模态,采用小波软阈值方法对这些模态进行滤波,最后对信号进行重构。经数值仿真与试验数据验证表明此方法是可行有效的,与FIR数字滤波器相比,本方法滤波效果较好:对不同输入信噪比的仿真信号进行滤波后,本方法的输出信噪比提高30%以上,滤波后所得信号与加噪前纯信号的相关系数最大提高22%;对实验数据进行滤波后,不同时间段信号的相关系数最大提高至1.6倍。
英文摘要:
      The purpose of this work is to study a method for filtering 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, analyzing the characteristic of the IMFs, and finding the main component IMFs, then the IMFs 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. The given method is proved feasibly and effectively by numerical simulation and experiment data, comparing with FIR digital filter method, the following acquaintances can be observed: (1) There is above 30% output signal noise ratio (SNR) improved for simulation signal under different input SNR, respectively. (2) The correlative coefficient between the signal filtered and the simulation signal without noise can be improved 22% at most. (3) The correlative coefficient between different periods of timeSfor experiment data can also be improved to 1.6 times at most.
DOI:10.11684/j.issn.1000-310X.2016.04.006
中文关键词: 经验模态分解,小波软阈值,高频信号,滤波,水声
英文关键词: Ensemble  empirical mode  decomposition (EEMD),Wavelet  soft threshold(WST),High  frequency signal,Filtering,Underwater  acoustic
基金项目:
作者单位E-mail
丁浩 海军潜艇学院 青岛 266000 dinghao1015@126.com 
赵建昕 海军潜艇学院 青岛 266000 zhao jianxin@126.com 
笪良龙 海军潜艇学院 青岛 266000 da lianglong@126.com 
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