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

Previous Articles     Next Articles

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

RICH HTML

0   

PDF (PC)

224

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

CLC Number: 

  • 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.
[1] YING Huan,LIU Song-hua,TANG Bo-wen,HAN Li-fang,ZHOU Liang. Efficient deterministic replay technique based on adaptive release strategy [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1917-1924.
[2] LIU Zhong-min,WANG Yang,LI Zhan-ming,HU Wen-jin. Image segmentation algorithm based on SLIC and fast nearest neighbor region merging [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1931-1937.
[3] SHAN Ze-biao,LIU Xiao-song,SHI Hong-wei,WANG Chun-yang,SHI Yao-wu. DOA tracking algorithm using dynamic compressed sensing [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1938-1944.
[4] YAO Hai-yang, WANG Hai-yan, ZHANG Zhi-chen, SHEN Xiao-hong. Reverse-joint signal detection model with double Duffing oscillator [J]. 吉林大学学报(工学版), 2018, 48(4): 1282-1290.
[5] QUAN Wei, HAO Xiao-ming, SUN Ya-dong, BAI Bao-hua, WANG Yu-ting. Development of individual objective lens for head-mounted projective display based on optical system of actual human eye [J]. 吉林大学学报(工学版), 2018, 48(4): 1291-1297.
[6] CHEN Mian-shu, SU Yue, SANG Ai-jun, LI Pei-peng. Image classification methods based on space vector model [J]. 吉林大学学报(工学版), 2018, 48(3): 943-951.
[7] CHEN Tao, CUI Yue-han, GUO Li-min. Improved algorithm of multiple signal classification for single snapshot [J]. 吉林大学学报(工学版), 2018, 48(3): 952-956.
[8] MENG Guang-wei, LI Rong-jia, WANG Xin, ZHOU Li-ming, GU Shuai. Analysis of intensity factors of interface crack in piezoelectric bimaterials [J]. 吉林大学学报(工学版), 2018, 48(2): 500-506.
[9] LIN Jin-hua, WANG Yan-jie, SUN Hong-hai. Improved feature-adaptive subdivision for Catmull-Clark surface model [J]. 吉林大学学报(工学版), 2018, 48(2): 625-632.
[10] WANG Ke, LIU Fu, KANG Bing, HUO Tong-tong, ZHOU Qiu-zhan. Bionic hypocenter localization method inspired by sand scorpion in locating preys [J]. 吉林大学学报(工学版), 2018, 48(2): 633-639.
[11] YU Hua-nan, DU Yao, GUO Shu-xu. High-precision synchronous phasor measurement based on compressed sensing [J]. 吉林大学学报(工学版), 2018, 48(1): 312-318.
[12] WANG Fang-shi, WANG Jian, LI Bing, WANG Bo. Deep attribute learning based traffic sign detection [J]. 吉林大学学报(工学版), 2018, 48(1): 319-329.
[13] LIU Dong-liang, WANG Qiu-shuang. Instantaneous velocity extraction method on NGSLM data [J]. 吉林大学学报(工学版), 2018, 48(1): 330-335.
[14] TANG Kun, SHI Rong-hua. Detection of wireless sensor network failure area based on butterfly effect signal [J]. 吉林大学学报(工学版), 2017, 47(6): 1939-1948.
[15] LI Juan, MENG Ke-xin, LI Yue, LIU Hui-li. Seismic signal noise suppression based on similarity matched Wiener filtering [J]. 吉林大学学报(工学版), 2017, 47(6): 1964-1968.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd