估计盲信源分离混合矩阵的通用方法

doi:10.3724/SP.J.1087.2012.02432

计算机应用 ›› 2012, Vol. 32 ›› Issue (09): 2432-2435.DOI: 10.3724/SP.J.1087.2012.02432

估计盲信源分离混合矩阵的通用方法

张延良^1*,张伟涛²,杜静静³

1.河南理工大学计算机科学与技术学院,河南焦作 454001;
2.西安电子科技大学电子工程学院,西安 710071;
3.河南理工大学电气工程与自动化学院,河南焦作 454001

收稿日期:2012-03-20 修回日期:2012-06-07 发布日期:2012-09-01 出版日期:2012-09-01
通讯作者: 张延良
作者简介:张延良(1979-),男,河南汝州人,副教授,博士,主要研究方向:盲信号处理、独立分量分析; 张伟涛(1983-),男,陕西户县人,讲师,博士,主要研究方向:通信信号处理; 杜静静(1982-),女,河南博爱人,副教授,博士,主要研究方向:非线性控制。
基金资助:
国家自然科学基金资助项目(60775013,61104079);河南理工大学青年基金资助项目(Q2011-50);河南理工大学博士基金资助项目(648753)

General method to estimate mixture matrix in blind source separation

ZHANG Yan-liang^1*,ZHANG Wei-tao²,DU Jing-jing³

1.College of Computer Science and Technology,Henan Polytechnic University,Jiaozuo Henan 454001,China;
2.School of Electronic Engineering,Xidian University,Xi'an Shaanxi 710071,China;
3.College of Electrical Engineering and Automation,Henan Polytechnic University,Jiaozuo Henan 454001,China

Received:2012-03-20 Revised:2012-06-07 Online:2012-09-01 Published:2012-09-01

摘要/Abstract

摘要： 混合矩阵的估计是解决盲信源分离问题的关键一步,但现有研究中缺乏一种同时适用于适定、超定及欠定情况下混合矩阵估计的通用方法。根据张量标准分解的因子矩阵和盲信源分离混合矩阵的估计均存在幅值和排列顺序的不确定性这一性质,将混合矩阵的估计转化为观测信号统计量所组成张量的标准分解问题,标准分解采用循环最小化方法,通过交替最小二乘算法实现。理论分析和仿真实验表明,所提方法可有效解决适定、超定和欠定混合矩阵的估计,是一种估计盲信源分离混合矩阵的通用方法。

关键词: 盲信源分离, 欠定混合, 标准分解, 循环最小化

Abstract: The estimation of mixture matrix is a key step to solve the problem of blind source separation. But there lacks a general estimation method suitable for well-determined, over-determined and under-determined mixture matrix in the existing research. Scaling and permutation ambiguities lie in both factor matrix of tensor canonical decomposition and mixture matrix in blind source separation. With this property, the estimation of mixture matrix can be transformed into tensor canonical decomposition of observed signals' statistics. The canonical decomposition can be implemented by cyclic minimization, with the algorithm of alternating least squares. The theoretical analysis and simulations show that the method proposed in this paper is a general method to estimate well-determined, over-determined and under-determined mixture matrix.

Key words: blind source separation, underdetermined mixture, canonical decomposition, cyclic minimization

中图分类号:

TN911.7

张延良张伟涛杜静静. 估计盲信源分离混合矩阵的通用方法[J]. 计算机应用, 2012, 32(09): 2432-2435.

ZHANG Yan-liang ZHANG Wei-tao DU Jing-jing. General method to estimate mixture matrix in blind source separation[J]. Journal of Computer Applications, 2012, 32(09): 2432-2435.

参考文献

[1]HYVARINEN A,KARHUNEN J,OJA E.Independent component analysis[M].New York:John Wiley & Sons,2001.
[2]CICHOCKI A,AMARI S I.Adaptive blind signal and image processing[M].New York:John Wiley & Sons,2002.
[3]COMON P,JUTTEN C.Handbook of blind source separation,independent component analysis and applications [M].London:Academic Press,2010.
[4]BOFILL P,ZIBULEVSKY M.Underdetermined blind source separation using sparse representations[J].Signal Processing,2001,81(11):2353-2362.
[5]CICHOCKI A,SABALA I,CHOI S,et al.Self-adaptive independent component analysis for sub-Gaussian and super-Gaussian mixtures with unknown number of source signals and additive noise[EB/OL].[2009-10-10].http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.215.776&rep=rep1&type=pdf.
[6]YE J M,ZHU X L,ZHANG X D.Adaptive blind source separation with an unknown number of sources[J].Neural Computation,2004,16(8):1641-1660.
[7]冶继民,张贤达,金海红.超定盲信号分离的半参数统计方法[J].电波科学学报,2006,21(3):331-336.
[8]LI X L,ZHANG X D.Non-orthogonal approximate joint diagonalization free of degenerate solution[J].IEEE Transactions on Signal Processing,2007,55(51):1803-1814.
[9]YEREDOR A.Non-orthogonal joint diagonalization in the least-squares sense with application in blind source separation[J].IEEE Transactions on Signal Processing,2002,50(7):1545-1553.
[10]LI YUANQING,AMARI S,CICHOCKI A,et al.Underdetermined blind source separation based on sparse representation[J].IEEE Transactions on Signal Processing,2006,54(2):423-437.
[11]AISSA-EL-BEY A,LINH-TRUNG N,ABED-MERAIM K,et al.Underdetermined blind separation of nondisjoint sources in the time-frequency domain[J].IEEE Transactions on Signal Processing,2007,55(3):897-907.
[12]PENG DEZHONG,XIANG YONG.Underdetermined blind source separation based on relaxed sparsity condition of sources[J].IEEE Transactions on Signal Processing,2009,57(2):809-814.
[13]de LATHAUWER L,CASTAING J.Blind identification of underdetermined mixtures by simultaneous matrix diagonalization[J].IEEE Transactions on Signal Processing,2008,56(3):1096-1105.
[14]张延良,楼顺天,张伟涛.欠定盲信源分离混合矩阵估计的张量分解方法[J].系统工程与电子技术,2011,33(8):1703-1706.
[15]张延良.线性混合盲信源分离的算法研究[D].西安:西安电子科技大学,2011,
[16]KOLDA T G,BADER B W.Tensor decompositions and applications[J].SIAM Review,2009,51(3):455-500.
[17]de LATHAUWER L.A survey of tensor methods[C]// Proceedings of IEEE International Symposium on Circuits and Systems.Piscataway,NJ:IEEE Press,2009:2773-2776.
[18]CICHOCKI A,ZDUNEK R,PHAN A H,et al.Nonnegative matrix and tensor factorizations [M].New York:Wiley,2009.
[19]STOICA P,SELN Y.Cyclic minimizers,majorization tech-niques,and the expectation-maximization algorithm:a refresher [J].IEEE Signal Processing Magazine,2004,21(1):112-114.
[20]STEGEMAN A,TEN B,JOS M F,et al.Sufficient conditions for uniqueness in CANDECOMP/PARAFAC and INDSCAL with random component matrices[J].Psychometrika,2006,71(2):219-229.
[21]WANG FUXIANG,LIU ZHONGKAN,ZHANG JUN.Nonorthogonal joint diagonalization algorithm based on trigonometric parameterization[J].IEEE Transactions on Signal Processing,2007,55(11):5299-5308.
[22]de LATHAUWER L,CASTAING J,CARDOSO J-F.Fourth-order cumulant-based blind identification of underdetermined mixtures[J].IEEE Transactions on Signal Processing,2007,55(6):2965-2973.

估计盲信源分离混合矩阵的通用方法

General method to estimate mixture matrix in blind source separation

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