基于集成经验模态分解的海杂波去噪  

The Sea Clutter De-noising Based on Ensemble Empirical Mode Decomposition

作　　者：行鸿彦[1,2,3] 朱清清[1,2,3] 

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]江苏省气象探测与信息处理重点实验室,江苏南京210044 [3]南京信息工程大学电子与信息工程学院,江苏南京210044

出　　处：《电子学报》2016年第1期1-7,共7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计划(No.BY2013007-02;No.BY2011112);江苏省高等学校科研成果产业化推进计划(No.JHB2011-15);江苏省"六大人才高峰"计划;江苏省"信息与通信工程"优势学科资助

摘　　要：针对实际海杂波信号非线性非平稳的特点,提出基于集成经验模态分解(EEMD)的海杂波去噪方法.利用EEMD将含有目标信号的海杂波数据分解成一系列从高频到低频的固有模态函数(IMF),通过各个IMF的自相关,分选出有用信号和噪声分量,对噪声占主导作用的IMF选用Savitzky Golay(SG)滤波方法进行消噪,将滤波后的模态分量和剩余的分量进行重构得到削噪后的信号.结合最小二乘支持向量机(LSSVM)建立混沌序列的单步预测模型,从预测误差中检测淹没在海杂波背景中的微弱信号,比较去噪前和去噪后的均方根误差,利用均方根误差评价去噪效果.实验结果表明,EEMD算法对海杂波数据去噪是有效的,去噪后所得的均方根误差0.0028比去噪前所得的均方根误差0.0119降低了一个数量级.In viewof the nonlinear and non-stationary sea clutter signal,we put forward a de-noising method of sea clutter based on ensemble empirical mode decomposition（ EEMD）. By EEMD,sea clutter data containing the target signal can be decomposed into a series of intrinsic mode function. Noise component uses the Savitzky Golay filter method for denoising. The mode components after filtering and the remaining components are reconstructed into a newsignal. Combined with least square support vector machine,single-step prediction model of chaotic sequence is set up. Compare the root mean square error before and after de-noising so that we can evaluate the denoising effect from the root mean square error. The experimental results showthat EEMD algorithm is effective for the de-noising of the sea clutter. By de-noising,the root mean square error can be reduced by one orders of magnitude,reaching 0. 0028,while the model before de-noising can reach only0. 0119.

关 键 词：海杂波 集成经验模态分解 自相关函数 Savitzky Golay滤波 

分 类 号：TN911.7[电子电信—通信与信息系统]