基于集成经验模态分解的海杂波去噪

doi:10.3969/j.issn.0372-2112.2016.01.001

电子学报 ›› 2016, Vol. 44 ›› Issue (1): 1-7.DOI: 10.3969/j.issn.0372-2112.2016.01.001

• 学术论文 • 下一篇

基于集成经验模态分解的海杂波去噪

行鸿彦^1,2,3, 朱清清^1,2,3

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

收稿日期:2014-05-06 修回日期:2014-07-15 出版日期:2016-01-25
- 作者简介:
- 行鸿彦 男,博士、教授、博士生导师,1962年8月出生于山西新绛.1983年于太原理工大学获得学士学位,1990年于吉林大学获得硕士学位,2003年于西安交通大学获得博士学位,研究方向:气象仪器设计与计量,信号检测与处理等. E-mail:xinghy@nuist.edu.cn 朱清清 女,硕士研究生,1990年3月出生于江苏省南通市,研究方向:微弱信号检测技术. E-mail:290217084@qq.com
- 基金资助:
- 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

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

XING Hong-yan^1,2,3, ZHU Qing-qing^1,2,3

1. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China;
2. Key Laboratory of Meteorological Observation and Information Processing of Jiangsu Province, Nanjing, Jiangsu 210044, China;
3. College of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China

Received:2014-05-06 Revised:2014-07-15 Online:2016-01-25 Published:2016-01-25
- Supported by:
- National Natural Science Foundation of China (No.61072133); Industry-University-Research Integration Innovation Project 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); Six Talents Peaks in Jiangsu Province; Funded by Information and Communication Engineering Priority Academic Program Development of Jiangsu Province

摘要/Abstract

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.

关键词: 海杂波, 集成经验模态分解, 自相关函数, Savitzky Golay滤波

Abstract:

In view of 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 de-noising.The mode components after filtering and the remaining components are reconstructed into a new signal.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 show that 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 only 0.0119.

Key words: the sea clutter, ensemble empirical mode decomposition, autocorrelation function, Savitzky Golay filter

中图分类号:

TN911.7

行鸿彦, 朱清清. 基于集成经验模态分解的海杂波去噪[J]. 电子学报, 2016, 44(1): 1-7.

XING Hong-yan, ZHU Qing-qing . The Sea Clutter De-noising Based on Ensemble Empirical Mode Decomposition[J]. Acta Electronica Sinica, 2016, 44(1): 1-7.

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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

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