自适应实时型复值混合信号盲分离算法研究

Journal of Systems Engineering and Electronics ›› 2009, Vol. 31 ›› Issue (5): 1018-1021.

自适应实时型复值混合信号盲分离算法研究

杨莘元¹, 赵立权¹, 张朝柱¹, 贾雁飞²

1. 哈尔滨工程大学信息与通信工程学院, 黑龙江, 哈尔滨, 150001;
2. 北华大学, 吉林, 吉林, 132013

收稿日期:2008-01-10 修回日期:2008-07-07 出版日期:2009-05-20 发布日期:2010-01-03
作者简介:杨莘元(1944- ),男,教授,博士生导师,主要研究方向为通信信号处理,电子对抗技术.E-mail:yangshenyuan@hrbeu.edu.cu

Research on adaptive real-time blind separation algorithm of complex mixed signals

YANG Shen-yuan¹, ZHAO Li-quan¹, ZHANG Chao-zhu¹, JIA Yan-fei²

1. Coll. of Information and Communication Engineering, Harbin Engineering Univ., Harbin 150001, China;
2. Beihua Univ., Jilin 132013, China

Received:2008-01-10 Revised:2008-07-07 Online:2009-05-20 Published:2010-01-03

摘要/Abstract

摘要： 利用非圆信号的协方差和伪协方差都为非零对角线矩阵的特点,构造代价函数,通过复值梯度推导得到一种针对复值混合信号的自适应实时算法.该算法能分离任何包含非圆信号源的复值混合信号;与强不相关变换算法相比,该算法结构简单,具有实时性,分离误差小,同时不需要特征值和奇异值分解,即使信源谱系数相同也能成功分离出复值混合信号.实验仿真证明了算法的有效性.

Abstract: Because the covariance and pseudo-covariance of non-circular signals are nonzero diagonal matrixes,the contrast function is constructed via the property.A new adaptive real-time algorithm for complex mixed signals is proposed through complex grads deduction.This algorithm can separate any mixed complex-value signals including non-circular signals.Compared with the strong-uncorrelating transform algorithm,it has the real-time feature,simple structure and less error.Moreover,eigenvalues and singularvalue decomposition are unnecessary,especially when the spectral coefficients are the same,the complex mixed signal can be still separated.Simulation results prove the efficiency of the proposed algorithm.

中图分类号:

TN911.72

杨莘元, 赵立权, 张朝柱, 贾雁飞. 自适应实时型复值混合信号盲分离算法研究[J]. Journal of Systems Engineering and Electronics, 2009, 31(5): 1018-1021.

YANG Shen-yuan, ZHAO Li-quan, ZHANG Chao-zhu, JIA Yan-fei. Research on adaptive real-time blind separation algorithm of complex mixed signals[J]. Journal of Systems Engineering and Electronics, 2009, 31(5): 1018-1021.

参考文献

[1] 马建仓,牛奕龙,陈海洋.盲信号处理[M].北京:国防工业出版社,2006.
[2] Hyvgrinen A,Karhunen J,Oja E.Independent component analysis[M].New York:Wiley Interscience,2001.
[3] 郑鹏,刘郁林,尤春艳,等.基于相关矩阵对角化和遗传算法的盲源分离法[J].系统工程与电子技术,2005,27(8);1361-1364.(Zheng Peng,Liu Yulin,You Chunyan,et al.Blind source separation method based on diagonalization of correlation matrices and genetic algorithm[J].Systems Engineering and Electronics,2005,27(8):1361-1364.)
[4] TichavskДP,KoldovskДZ,Oia E.Speed and accuracy enhancement of linear ICA techniques using rational nonlinear functions[C]//Inde pendent Component Analysis and Signal Separation.Proceedings 7^th International Conference,ICA 2007.Berlin,Germany,2007.
[5] 张安清,仇天爽,章新华.扩展联合对角化法的水声信号盲分离算法[J].系统工程与电子技术,2003,25(9):1058-1083.(Zhang Anqing,Qiu Tianshuang,Zhang Xinhua.Blind separation technique for multipath underwater acoustic signals based on extended Joint diagonalization method[J].Systems Engineering and Electronics,2003,25(9):1058-1083.)
[6] 吾文妍,张立明.带约束的卷积语音信号频域盲分离算法[J].系统工程与电子技术,2006,28(11):1617-1620.(Wu Wenyan,Zhang Liming.Blind source separation scheme with restrictive condition for convolutive audio signals in frequency-domain[J].Systems Engineering and Electronics,2006,28(11);1617-1620.)
[7] 谢德光,张贤达,李细林,等.基于独立分量分析的雷达目标识别方法[J].系统工程与电子技术,2007,29(2);164-166.(Xie Deguang,Zhang Xianda,Li Xiling,et al.Radar target recognition method based on independent component analysis[J].Systerns Engineering and Electronics,2007,29(2):164-166.)
[8] Cardoso J F,Souloumiac A.Blind beamforming from non-gaussian signals[J].IEEE Proc.of Radar Signal Proc,1993,140(6):362-370.
[9] A fast fixed-point algorithm for independent component analysis of complex valued signals[J].International Journal of Neural Systems,2000,10(1):1-8.
[10] 倪晋平,马远良,杨坤德.水声信号盲分离的实数算法与复数算法[J].西北工业大学学报,2002,20(1);44-48.
[11] 李小军,楼顺天,张贤达.一种针对复值信号的独立分量分析方法[J].西安电子科技大学学报(自然科学版),2005,32(3):447-451.
[12] Eriksson J,Koivunen V.Complex-valued ICA using second order statistics[J].IEEE Workshop Machine Learning Signal Processing,Sao Luis,Brazil,2004:183-191.
[13] Eriksson J,Koivunen V.Complex random vectors and ICA models:Identifiability uniqueness and separability[J].IEEE Trans.on Info Theory,2006,3(52):1017-1029.

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