基于CEEMDAN的雷达信号脉内细微特征提取法

王文哲; 吴华; 王经商; 张强

doi:10.13700/j.bh.1001-5965.2016.0410

基于CEEMDAN的雷达信号脉内细微特征提取法

doi: 10.13700/j.bh.1001-5965.2016.0410

空军工程大学航空航天工程学院, 西安 710038

基金项目: 国家自然科学基金（61379104）；航空科学基金（20152096019）

详细信息

作者简介:
王文哲,男,硕士研究生。主要研究方向:雷达信号处理、电子对抗理论与技术。Tel.:029-84787681,E-mail:524940802@qq.com;吴华,女,硕士,副教授。主要研究方向:雷达辐射源识别、电子对抗理论与技术。Tel.:029-84787681,E-mail:15129291728@163.com

通讯作者:
吴华,Tel.:029-84787681,E-mail:15129291728@163.com

中图分类号: TN97
计量
- 文章访问数: 1019
- HTML全文浏览量: 131
- PDF下载量: 759
- 被引次数: 0
出版历程
- 收稿日期: 2016-05-17
- 修回日期: 2016-07-16
- 网络出版日期: 2016-11-20

Subtle intrapulse feature extraction based on CEEMDAN for radar signals

Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi'an 710038, China

摘要

摘要: 有效的信号特征提取是高精度雷达辐射源识别的基础，以脉冲描述字为代表的传统特征已无法满足复杂电磁环境的需要。本文提出一种基于自适应噪声完备集合经验模态分解（CEEMDAN）的有效雷达辐射源脉内细微特征提取算法。雷达信号由对非平稳、非线性信号尤为有效的CEEMDAN分解产生的个别分量重构，抑噪效果通过1 000次蒙特卡罗实验得到验证，同时设计基于该重构的一种脉内特征空间。本文方法与主流特征提取方法的识别精度在6部雷达辐射源产生的3 000个不同脉内调制的加噪信号样本上进行了实验对比，结果表明不同种类信号样本在本文特征空间中清晰可分，本文方法较之主流方法更加精确，尤其在0 dB信噪比（SNR）下仍保持90%以上的高精度。
- 特征提取 /
- 自适应噪声完备集合经验模态分解(CEEMDAN) /
- 经验模态分解 /
- 噪声协助 /
- 雷达辐射源识别 /
- 信号重构
Abstract: Effective signal feature extraction builds the foundation of highly accurate radar emitter identification, a key function of the electronic warfare. Conventional features used in practice such as the pulse description words cannot fulfill the task in complex electromagnetic environments. An effective subtle intrapulse radar feature extraction method based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was proposed. Radar signals were reconstructed by components provided by the CEEMDAN decomposition process, which was highly effective for non-stationary and nonlinear signals; the de-noising effect of the reconstruction on radar signals was validated through 1 000 Monte Carlo experiments, and an intrapulse feature space based on the reconstruction was designed. The identification accuracy of the proposed feature space was compared to that of the mainstream methods in the area, on 3 000 noise-contaminated signal samples supposed to be generated by 6 radar emitters, with different intrapulse modulation. Experimental results show that the samples are totally distinguishable in the proposed feature space, and the proposed method is more accurate in the comparison, especially in highly noisy environment, with accuracy above 90% at 0 dB signal to noise ratio (SNR).
- feature extraction /
- complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) /
- empirical mode decomposition /
- noise assisted /
- radar emitter identification /
- signal reconstruction

HTML全文

参考文献(16)

[1]	MONTAZER G,KHOSHNIAT H,FATHI V.Improvement of RBF neural networks using fuzzy-OSD algorithm in an online radar pulse classification system[J].Applied Soft Computing,2013,13(9):3831-3838.
[2]	JIANG H,PANG Z,TANG P,et al.Intrapulse modulation recognition based on pulse description words[C]//Proceedings of the 2013 6th International Congress on Image and Signal Processing.Piscataway,NJ:IEEE Press,2013,3:1367-1371.
[3]	MATUSZEWSKI J,PARADOWSKI L.The knowledge based approach for emitter identification[C]//Proceedings of the 12th International Conference on Microware & Radar,MIKON '98.Piscataway,NJ:IEEE Press,1998,3:810-814.
[4]	刘海军,柳征,姜文利,等.基于联合参数建模的雷达辐射源识别方法[J].宇航学报，2011,32(1):142-149.LIU H J,LIU Z,JIANG W L,et al.A joint-parameter modeling based radar emitter identification method[J].Journal of Astronautics,2011,32(1):142-149(in Chinese).
[5]	关一夫,张国毅.一种基于隐马尔科夫模型的雷达辐射源识别方法[J].火力与指挥控制,2015,40(10):98-103.GUAN Y F,ZHANG G Y.A radar emitter recognition algorithm based on hidden markov models[J].Fire Control & Command Control,2015,40(10):98-103(in Chinese).
[6]	XIAO W,WU H,YANG C.Support vector machine radar emitter identification algorithm based on AP clustering[C]//Proceedings of the 2013 International Conference on Quality,Reliability,Risk maintenance,and Safety Engineering (QR2MSE).Piscataway,NJ:IEEE Press,2013:2062-2064.
[7]	SINGH A,RAO K.Digital receiver-based electronic intelligence system configuration for the detection and identification of intrapulse modulated radar signals[J].Defence Science Journal,2014,64(2):152-158.
[8]	KAWALEC A,OWCZAREK R.Radar emitter recognition using intrapulse data[C]//Proceedings of the 15th International Conference on Microwaves,Radar and Wireless Communications,2004,MIKON-2004.Piscataway,NJ:IEEE Press,2004,2:435-438.
[9]	PU Y W,JIN W D,ZHU M,et al.Classification of radar emitter signal based on cascade feature extraction and hierarchical decision technique[C]//Proceedings of the 8th International Conference on Signal Processing.Piscataway,NJ:IEEE Press,2006.
[10]	ZHU B,JIN W D.Feature analysis of advanced radar emitter signals based on continuous wavelet transform[J].Applied Mechanics and Materials,2013,246-247:1125-1129.
[11]	PENG Z,TSE P,CHU F.A comparison study of improved Hilbert-Huang transform and wavelet transform:Application to fault diagnosis for rolling bearing[J].Mechanical Systems and Signal Processing,2005,19(5):974-988.
[12]	TORRES M,COLOMINAS M,SCHOLOTTHAUER G,et al.A complete ensemble empirical mode decomposition with adaptive noise[C]//Proceedings of the 2011 IEEE International Conference on Acoustics,Speech and Signal Processing.Piscataway,NJ:IEEE Press,2011:4144-4147.
[13]	HUANG N E,SHEN Z,LONG S,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[C]//Proceedings of the Royal Society of London A:Mathematical,Physical and Engineering Sciences.London:The Royal Society,1998,454(1971):903-995.
[14]	WU Z H,HUANG N E.Ensemble empirical mode decomposition:A noise-assisted data analysis method[J].Advances in Adaptive Data Analysis,2009,1(1):1-41.
[15]	HAHN S L.Hilbert transforms in signal processing[M].Norwood:Artech House on Demand,1996:1-55.
[16]	HUANG N E.Computing instantaneous frequency by normalizing Hilbert transform:US,6901353[P].2005-05-31.