吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (4): 1329-1335.doi: 10.13229/j.cnki.jdxbgxb201504044

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基于欠定盲源分离的电磁干扰分离方法

郭慧1, 2, 付永庆1, 苏东林2, 刘焱2   

  1. 1.哈尔滨工程大学 信息与通信工程学院, 哈尔滨 150001;
    2.北京航空航天大学 电磁兼容技术研究所, 北京 100191
  • 收稿日期:2013-12-03 出版日期:2015-07-01 发布日期:2015-07-01
  • 通讯作者: 付永庆(1956-),男,教授.研究方向:混沌通信,盲信号处理,图像处理.E-mail:fuyongqing@hrbeu.edu.cn
  • 作者简介:郭慧(1987-),女,博士研究生.研究方向:盲信号处理.E-mail:chinamengh823@126.com
  • 基金资助:
    国家自然科学基金项目(61172038,60831001)

Method to separate electromagnetic interference sources based on underdetermined blind sources separation

GUO Hui1, 2, FU Yong-qing1, SU Dong-lin2, LIU Yan2   

  1. 1.College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001,China;
    2.EMC Laboratory, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
  • Received:2013-12-03 Online:2015-07-01 Published:2015-07-01

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摘要： 针对传统电磁干扰测试方法无法对多个同时工作的机载设备进行独立观测,且现有的盲源分离算法对观测信号数目少于源信号数目的情况无效,提出了一种欠定盲源分离算法用于电磁干扰分离。该方法适用于具有稀疏特性的谐波信号,将干扰源看作源信号,首先采用邻域比值法提取混合信号的单源主导区间,提高信号的稀疏特性,然后在此区间采用Hough加窗法对电磁干扰源的数目和混合信道进行估计,避免算法陷入局部最大,最后采用夹角差排序法选择合适的混合矩阵列向量来确定分离矩阵,将欠定方程转变成正定方程,实现混合信号的分离。仿真实验得到的分离干扰信号与原始干扰信号间的相关系数平均值为0.9936,表明算法具有较高的准确性,Monte Carlo仿真结果表明本文算法较几种常用算法具有更好的抗噪声性;实测实验对实测数据分离并整改,整改结果表明了本文算法的可行性。

关键词: 信息处理技术, 欠定盲源分离, 电磁干扰信号, 单源主导区间, Hough加窗法, 夹角差排序法

Abstract: Traditional testing methods of electromagnetic interferences can not observe individual airborne equipment when multiple devices are working. Furthermore, the existing blind sources separation algorithms can not solve the problem that the number of observed signals is less than the number of source signals. To overcome these shortcomings, a new underdetermined blind sources separation algorithm is proposed to separate electromagnetic interferences. The method is applied to harmonic signals with sparse characteristics. The algorithm constructs mathematical abstraction of electromagnetic interferences by underdetermined blind source separation mode. First, the single source area is found by calculating the ratio of observed sampling points. Then, the number of sources and mixture matrix are estimated using Hough-windowed method. Finally, the mixed signals are separated based on angle difference sorting method. Simulation results show that the effectiveness and accuracy of the proposed algorithm that the average correlation coefficient between separated signals and sources is 0.9936. Monte Carlo simulation results show the higher stability and noise immunity, and measured results demonstrate the feasibility of the algorithm.

Key words: information processing, underdetermined blind sources separation, electromagnetic interference signals, single source area, Hough-windowed method, angles' differentials sort method

中图分类号: 

  • TN911.7
[1] Prasad Kodali V. 工程电磁兼容[M]. 陈淑凤, 高攸纲, 苏东林, 周璧华译. 北京: 人民邮电出版社, 2006: 5-8.
[2] Pham D T, Cardoso J F. Blind separation of instantaneous mixtures of non stationary sources[J]. IEEE Transactions on Signal Processing, 2000, 49(9): 1837-1848.
[3] Reyhani N, Ylipaaavalniemi J, Vigario R, et al. Consistency and asymptotic normality of FastICA and bootstrap FastICA[J]. Signal and Processing, 2012, 92(8): 1767-1778.
[4] Cardoso J F, Souloumiac A. Blind beamforming for non-Gaussian signals[J]. IEEE Proceedings-F, 1993, 140(6): 362-370.
[5] Badawi W K M,Chibelushi C C, Patwary M N. et al. Specular-based illumination estimation using blind signal separation techniques[J]. IET Image Processing, 2012, 6(8): 1181-1191.
[6] Mammone N,La Foresta F,Morabito F C.Automatic artifact rejection from multichannel scalp EEG by wavelet ICA[J].IEEE Sensors Journal,2012,12(3):533-542.
[7] 陈晓军, 成昊, 唐斌. 基于ICA的雷达信号欠定盲分离算法[J]. 电子与信息学报, 2010, 32(4): 919-924. Chen Xiao-jun, Chen Hao, Tang Bin. Underdetermined blind radar signal separation based on ICA[J]. Journal of Electronics & Information Technology, 2010, 32(4): 919-924.
[8] Ilin A, Valpola H, Oja E. Semiblind source separation of climate data detects E1 Nino as the component with the highest variability[C]∥Proceedings of International Joint Conference on Neural Networks, Montreal, Canada, 2005: 1722-1727.
[9] Takahata A K, Nadalin E Z, Ferrari R, et al. Unsupervised processing of geophysical signals: a review of some key aspects of blind deconvolution and blind source separation[J]. IEEE Signal Processing Magazine, 2012, 29(4): 27-35.
[10] Shindo H, Hirai Y. Blind source separation by a geometrical method[C]∥Proceeding of the International Joint Conference on Neural Networks, Honolulu,2002: 1108-1114.
[11] 郭斯羽, 孔亚广, 张熙芳. 基于Hough变换的角点检测算法[J]. 仪器仪表学报, 2008, 29(11): 2424-2429. Guo Si-yu, Kong Ya-guang, Zhang Xi-fang. Corner detection algorithm based on Hough transform[J]. Chinese Journal of Scientific Instrument, 2008, 29(11): 2424-2429.
[12] Onoda T, Sakai M, Yamada S. Careful seeding method based on independent components analysis for k-means clustering[J]. Journal of Emerging Technologies in Web Intelligence, 2012, 4(1): 51-59.
[13] Sun H J, Wang S R, Jiang Q S. FCM-based model selection algorithm for determining the number of cluster[J]. Pattern Recognition, 2004, 37(10): 2027-2037.
[14] Bofill P, Zibulevsky M. Underdetermined blind source separation using sparse representations[J]. Signal Processing, 2001, 81(11): 2353-2362.
[15] Li Y, Amari S I, Cichocki A. Underdetermined blind source separation based on sparse representation[J]. IEEE Transactions on Signal Processing, 2006, 54(2): 423-437.
[16] Aissa-El-Bey A, Linh-Trung N. Underdetermined blind separation of nondisioint sources in the time-frequency domain[J]. IEEE Transaction on Signal Processing, 2007, 55(3): 897-907.
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