基于变分模态分解的自适应滤波降噪方法
An Adaptive Filtering Denoising Method Based on Variational Mode Decomposition
- 电子学报 2021年49卷第8期 页码：1457-1465
- 作者机构：
1.哈尔滨工业大学电子与信息工程学院，黑龙江哈尔滨 150001
2.华为技术有限公司北京研究所，北京 100095
- 作者简介：
[ "吴龙文 男，1988年9月出生.工程师，工学博士，主要研究方向为辐射源识别、辐射源个体识别、无源定位、多核学习和医学信号处理. E-mail：wulongwen@hit.edu.cn" ]
[ "聂雨亭 女，1997年9月出生.硕士研究生，主要研究方向为时频分析、信号处理和医学信号处理. E-mail：yuting.nie@foxmail.com" ]
[ "张宇鹏 男，1992年8月出生.工学硕士，主要研究方向为时频分析、信号处理和雷达信号处理. E-mail：zhangyupeng10@hisilicon.com" ]
[ "何胜阳 男，1983年2月出生.高级工程师，工学博士，主要研究方向为无线光通信、嵌入式系统和算法加速. E-mail：heshengyang@hit.edu.cn" ]
[ "赵雅琴（通信作者） 女，1976年2月出生.教授，工学博士，主要研究方向为辐射源识别、辐射源个体识别、无源定位、光通信、医学信号处理. E-mail：yaqinzhao@hit.edu.cn" ]
- 基金信息：国家自然科学基金面上项目(61671185;62071153)
DOI：10.12263/DZXB.20190972
中图分类号： TN971.+1;收稿：2019-08-26，
修回：2020-10-15，
纸质出版：2021-08-25
- 稿件说明：
移动端阅览
吴龙文,聂雨亭,张宇鹏等.基于变分模态分解的自适应滤波降噪方法[J].电子学报,2021,49(08):1457-1465.
WU Long-wen,NIE Yu-ting,ZHANG Yu-peng,et al.An Adaptive Filtering Denoising Method Based on Variational Mode Decomposition[J].ACTA ELECTRONICA SINICA,2021,49(08):1457-1465.
吴龙文,聂雨亭,张宇鹏等.基于变分模态分解的自适应滤波降噪方法[J].电子学报,2021,49(08):1457-1465. DOI： 10.12263/DZXB.20190972.
WU Long-wen,NIE Yu-ting,ZHANG Yu-peng,et al.An Adaptive Filtering Denoising Method Based on Variational Mode Decomposition[J].ACTA ELECTRONICA SINICA,2021,49(08):1457-1465. DOI： 10.12263/DZXB.20190972.
摘要
为了提高分析信号的信噪比，本文提出了一种基于变分模态分解的变步长归一化最小均方自适应滤波降噪方法.该方法对原信号进行变分模态分解并区分信号分量和噪声分量，再对噪声分量进行间隙阈值降噪处理并将其作为参考信号输入自适应滤波器，通过自适应算法迭代处理得到降噪后的信号分量，并通过重构算法得到最终降噪后的信号.本文还在变分模态分解的基础上使用小波阈值降噪和间隙阈值降噪方法按不同方案进行降噪处理并得到最佳算法，将其与所提算法进行对比.实验结果表明，本文所提自适应滤波降噪方法的降噪效果比阈值降噪最佳方法效果更好.
Abstract
To improve the SNR of received signals
a normalized minimum mean square adaptive filtering denoising method based on variational mode decomposition using variable step-size was proposed. The proposed algorithm decomposed the original signal into several components labelled as noise or signal component. Then an interval threshold denoising method was exploited to denoise the noise component before inputted into an adaptive filter as a reference signal. All the rest signal components were used to reconstruct the final denoised signal after denoised by iterative adaptive filters. In addition
an optimal algorithm based on variational mode decomposition using wavelet threshold denoising and interval threshold denoising methods was exploited. Experimental results show that the proposed adaptive filtering denoising method outperforms the optimal algorithm using threshold denoising.
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references
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