Adaptive Denoising Algorithm Based on the Variance Characteristics of EMD

ZHANG Qiang; XING Hong-yan

doi:10.3969/j.issn.0372-2112.2015.05.010

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Adaptive Denoising Algorithm Based on the Variance Characteristics of EMD

更新时间：2025-07-16

- Adaptive Denoising Algorithm Based on the Variance Characteristics of EMD
- Acta Electronica Sinica Vol. 43, Issue 5, Pages: 901-906(2015)
- 作者机构：
  
  1. 南京信息工程大学气象灾害预报预警与评估协同创新中心,江苏,南京,210044
  2. 南京信息工程大学江苏省气象探测与信息处理重点实验室,江苏,南京,210044
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61072133);Industry-University-Research Integration Innovation Fund of Jiangsu Province (No.BY2013007-02, No.BY2011112);Industrialization Promotion Program of Scientific Research Achievemnet of colleges and universities in Jiangsu Province (No.JHB2011-15);Information and Communication Engineering Priority Academic Program Development of Jiangsu Province
- DOI：10.3969/j.issn.0372-2112.2015.05.010
  CLC： TN911.7
- Published：2015
- 稿件说明：
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ZHANG Qiang, XING Hong-yan. Adaptive Denoising Algorithm Based on the Variance Characteristics of EMD[J]. Acta Electronica Sinica, 2015, 43(5): 901-906.
DOI：

ZHANG Qiang, XING Hong-yan. Adaptive Denoising Algorithm Based on the Variance Characteristics of EMD[J]. Acta Electronica Sinica, 2015, 43(5): 901-906. DOI： 10.3969/j.issn.0372-2112.2015.05.010.

摘要

本文利用经验模态分解算法(EMD)

研究了不同状态下混沌信号的方差特性

提出了一种EMD分解层数自适应的去噪算法.该算法根据固有模态函数(IMF)方差最大值对应层数与总分解层数的关系

能够自适应选择需处理的IMF层数

并结合提升小波在更新和预测方面的优势综合去噪

分别以Lorenz、Chen系统(加入10%-100%的高斯白噪声)和实测的IPIX雷达数据作为混沌背景噪声进行了实验研究.结果表明:在不同程度的低噪声(30%)环境下

与传统小波阈值去噪等方法相比

其均方误差降低了30%以上

信噪比提高了1.5db-3.5db

并能有效地去除海杂波噪声

提高混沌背景下的微弱信号检测效果.

Abstract

This paper studies the variance characteristics of chaotic signal in different conditions and puts forward an adaptive denoising algorithm on account of EMD decomposition layers

by using the Empirical Mode Decomposition (EMD).The arithmetic can adaptively select the IMF layer which needs to be processed

based on the relationship between the maximum variance corresponding layers and the total number of decomposition layers of intrinsic mode function (IMF)

and it also can make intergrated denosing by making use of the lifting wavelet's advantages in the field of updating and predicting.It carried out the experimental study

based on the chaotic background noise from Lorenz and Chen System(adding 10%-100% white gaussian noise) and the measured IPIX radar data.The result shows that:under varying degrees of low noise (30%)

it decreases the error of mean square by at least 30% compared with the methods such as traditional wavelet threshold denoising

and the signal to noise ratio has increased by 1.5db-3.5db

and can effectively reduce the sea clutter noise to increase the detection effect under the background of chaos.

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