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超宽带通信信号欠定盲源分离 被引量：2

Underdetermined Blind Source Separation Applied to the Ultra-Wideband Communication Signals
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摘要 针对超宽带通信信号,提出了一种新的二阶段欠定盲源分离方法:阶梯图-最小角度法。在第一阶段即混叠矩阵估计阶段,提出用阶梯图对均匀抽样的数据点进行聚类计算,建立了一种估计混合矩阵的工程化模型,它可以直接估计出源信号个数,并且得到精度较高的混合矩阵估计值;在第二阶段借助最小角度分离准则估计出源信号,从而获得较好的分离性能。 A novel two-stage method of underdetermined blind source separation named ladder diagram-minimum angle is proposed for the ultra-wideband communications signals.In the first stage,where the mixing matrix is estimated,cluster calculation is performed on the uniform sampling of data points by using ladder diagram,thus a engineering model for estimating the mixing matrix has been established,which is used to directly estimate the number of source signals and get high precision estimates of the mixing matrix.In the second stage,the source signal is estimated by using the minimum angle separation criterion and the better separation performance is thereby obtained.
作者 贾银洁 许鹏飞
机构地区 淮阴工学院计算机工程学院 西安科技大学通信工程学院 中兴通讯中心研究院
出处 《南京邮电大学学报（自然科学版）》 2011年第1期23-28,共6页 Journal of Nanjing University of Posts and Telecommunications：Natural Science Edition
关键词 超宽带通信信号 欠定盲源分离 稀疏分量分析 阶梯图 最小角度 ultra-wideband communication signals underdetermined blind source separation sparse component analysis ladder diagram minimum angle
分类号 TN911.6 [电子电信—通信与信息系统]
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参考文献12

  • 1CARDOSO J F. Super-symmetric decomposition of the fourthorder cumulant tenser: Blind identification of more sources than sensors [ C ]//Proc IEEE ICASSP-91. Piscataway: IEEE, 1991:3109 - 3112.
  • 2SHAMSUNDER S, GIANNAKIS G B. Modeling of non-Gaussian array data using cumulants : DOA estimation of more sources with less sensors [ J ]. Signal Processing, 1993,30 (3) :279 - 297.
  • 3TALEB A, JUTTFEN C. On underdetermined source separation [ C ] // Proc ICASSP. Piscataway : IEEE, 1999 : 1445 - 1448.
  • 4LI Y Q,ANDRZEJ C,AMARI S. Analysis of sparse representation and blind source separation [ J ]. Neural Comput ,2004 ,16 : 1193 - 1234.
  • 5LEE T W, LEWICKI M S, GIROLAMI M, et al. Blind source separation of more sources than mixtures using overcomplete representations [ J ]. Signal Process Lett, 1999,6 ( 4 ) : 87 - 90.
  • 6ZIBULEVSKY M, PEARLMUTTER B A. Blind Source Separation by Sparse Decomposition[J]. Neural Comput,2001,13(4) :863-882.
  • 7BOFILL P,ZIBULEVSKY M. Underdetermined source separation using sparse representations [ J ]. Signal Proeessing, 2001,81 ( 11 ) : 2353 - 2362.
  • 8THEIS F J, JUNG A,PUNTONET C G, et al. Linear geometric ICA: fundamentals and algorithms [ J ]. Neural Computation, 2003, 15 (2) :419 -439.
  • 9LI Y, AMARI S I, CICHOCKI A. Underdetermined blind source separation based on sparse representation[ J]. Signal Processing,2006, 54(2) :423 -437.
  • 10李荣华,杨祖元,赵敏,谢胜利.基于支持向量机的欠定盲分离[J].电子与信息学报,2009,31(2):319-322. 被引量：4

二级参考文献12

  • 1Comon P. Independent component analysis: A new concept [J]. Signal Processing, 1994, 36(3): 287-314.
  • 2Xie S L, He Z S, and Fu Y L. A note on Stoners conjecture of blind signal separation [J]. Neural Computation, 2005, 17(2): 321-330.
  • 3Bofill P and Zibulevsky M. Underdetermined blind source separation using sparse representations [J]. Signal Processing, 2001, 81(11): 2353-2362.
  • 4Li Y Q, Cichocki A, and Amari S. Analysis of sparse representation and blind source separation [J]. Neural Computation, 2004, 16(6): 1193-1234.
  • 5Li Y Q, Amari S, and Cichocki A, et al.. Underdetermined blind source separation based on sparse representations [J]. IEEE Trans. on Signal Processing, 2006, 54(2): 423-437.
  • 6Michael S L and Terrence J S. Learning overcomplete representations [J]. Neural Computation, 2000, 12(2): 337-365.
  • 7He Z S and Cichocki A. K-EVD clustering and its applications to sparse component analysis [C]. Independent Component Analysis and Blind Signal Processing, Charleston SC, USA, Mar. 5-8, 2006: 90-97.
  • 8Vapnik V. The Nature of Statistical Learning Theory [M]. New York, Spring Verlag, 1995: 30-105.
  • 9Platt J C. Sequential minimal optimization A fast algorithm for training support vector machines [C]. In: SchOlkopf B, Burges C J C, and Smola A J. (Eds.): Advances in Kernel Methods-Support Vector Learning. MIT Press, Cambridge, MA, 1998: 185-208.
  • 10Xie S L, He Z S, and Gao Y. Adaptive Theory of Signal Processing [M]. 1st ed, Beijing, Chinese Science Press, 2006: 103-129.

共引文献3

同被引文献10

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二级引证文献12

南京邮电大学学报（自然科学版）
2011年 第1期

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