Blind Separation of Noncircular Sources Via Approximate Joint Diagonalization of Augmented Charrelation Matrices

被引：0
|
作者：
Xiaoming Gou
Zhiwen Liu
Jingyan Ma
Yougen Xu
机构：
[1] Beijing Institute of Technology,School of Information and Electronics
来源：
Circuits, Systems, and Signal Processing | 2015年 / 34卷
关键词：
Blind source separation; Characteristic function; Hessian matrix; Joint diagonalization; Noncircularity;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
An augmented charrelation matrix (ACM), which can utilize both the conventional and the conjugate statistical information in the complex domain, is proposed. The ACM additionally makes use of the conjugate Hessian matrix (namely conjugate charrelation matrix) of the observations of noncircular sources. A blind separation scheme built on the approximate joint diagonalization (AJD) principle is introduced, which precedes some numerical examples to demonstrate the improved performance of the ACM-AJD approach compared with some algorithms in the literature.
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收藏
页码：695 / 705
页数：10
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