| 基于协方差矩阵同时对角化的盲信号分离算法 |
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| 引用本文： | 刘实,王振力,张雄伟,陶然.基于协方差矩阵同时对角化的盲信号分离算法[J].北京理工大学学报,2007,27(10):919-923. |
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| 作者姓名： | 刘实 王振力 张雄伟 陶然 |
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| 作者单位： | 北京理工大学,信息科学技术学院电子工程系,北京,100081;解放军理工大学 |
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| 摘 要： | 提出了基于自相关协方差矩阵同时对角化的两个盲源信号分离算法. 利用广义奇异值分解(GSVD)算法,将源信号观测数据预白化后的零阶和一阶自相关协方差矩阵同时对角化,估算出两路源信号. 与二阶盲识别(SOBI)算法进行了比较,该算法具有计算简单且运算精度高的优点. 在线性混合加噪模型下,计算机仿真表明该算法的有效性.
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| 关 键 词： | 盲信号分离 广义奇异值分解 同时对角化 协方差矩阵 |
| 文章编号： | 1001-0645（2007）10-0919-05 |
| 收稿时间： | 2007/2/20 0:00:00 |
| 修稿时间： | 2007-02-20 |
Blind Source Separation Algorithm Based on Simultaneous Diagonalization of Covariance Matrices |
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| LIU Shi,WANG Zhen-li,ZHANG Xiong-wei and TAO Ran.Blind Source Separation Algorithm Based on Simultaneous Diagonalization of Covariance Matrices[J].Journal of Beijing Institute of Technology(Natural Science Edition),2007,27(10):919-923. |
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| Authors: | LIU Shi WANG Zhen-li ZHANG Xiong-wei and TAO Ran |
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| Institution: | 1. Department of Electronic Engineering, School of Information Science and Technology, Beijing Institute of Technology, Beijing 100081, China; 2. Department of Electronic Information Engineering, Institute of Communications Engineering, PLA University of Science and Technology, Nanjing, Jiangsu 210007, China |
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| Abstract: | A blind source separation(BSS) algorithm based on simultaneous diagonalizing autocorrelation covariance matrices is presented for two source signals.The proposed algorithm,which uses generalized singular value decomposition(GSVD) algorithm,simultaneously diagonalizes the autocorrelation covariance matrix and its one-sample delayed counterpart of the prewhitened source observation data.The desired source signals are finally computed.Compared with secord-order blind identification(SOBI) algorithm,the new algorithm requires simple computation and has higher computation precision.Under the condition of added noise to the linear mixed model,computer simulation results show the new algorithm's effectiveness. |
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| Keywords: | blind source separation(BSS) generalized singular value decomposition(GSVD) simultaneous diagonalization covariance matrix |
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