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

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.