基于EMD方差特性的混沌信号自适应去噪算法  

Adaptive Denoising Algorithm Based on the Variance Characteristics of EMD

作　　者：张强[1,2] 行鸿彦[1,2] 

机构地区：[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

出　　处：《电子学报》2015年第5期901-906,共6页Acta Electronica Sinica

基　　金：国家自然科学基金(No.61072133);江苏省产学研联合创新资金计划(No.BY2013007-02;No.BY2011112);江苏省高校科研成果产业化推进项目(No.JHB2011-15);江苏省"信息与通信工程"优势学科

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.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 corre- sponding layers and the total number of decornposition 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 carded out the experimental study,based on the chaotic background noise from Lorenz and Chen System（adding 10%-100% white gaussian noise） and the measured 1PIX 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.

关 键 词：经验模态分解 混沌 方差特性 提升小波 自适应去噪 

分 类 号：TN911.7[电子电信—通信与信息系统]