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

Xiaoming Gou; Zhiwen Liu; Jingyan Ma; Yougen Xu

doi:10.1007/s00034-014-9867-5

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

Xiaoming Gou, Zhiwen Liu, Jingyan Ma, Yougen Xu^*

^*此作品的通讯作者

集成电路与电子学院

Beijing Institute of Technology

科研成果: 期刊稿件 › 文章 › 同行评审

1 引用（Scopus）

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.

源语言	英语
页（从-至）	695-705
页数	11
期刊	Circuits, Systems, and Signal Processing
卷	34
期	2
DOI	https://doi.org/10.1007/s00034-014-9867-5
出版状态	已出版 - 2月 2015

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Gou, X., Liu, Z., Ma, J., & Xu, Y. (2015). Blind Separation of Noncircular Sources Via Approximate Joint Diagonalization of Augmented Charrelation Matrices. Circuits, Systems, and Signal Processing, 34(2), 695-705. https://doi.org/10.1007/s00034-014-9867-5

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abstract = "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.",

keywords = "Blind source separation, Characteristic function, Hessian matrix, Joint diagonalization, Noncircularity",

author = "Xiaoming Gou and Zhiwen Liu and Jingyan Ma and Yougen Xu",

note = "Publisher Copyright: {\textcopyright} 2014, Springer Science+Business Media New York.",

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doi = "10.1007/s00034-014-9867-5",

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AU - Xu, Yougen

PY - 2015/2

Y1 - 2015/2

N2 - 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.

AB - 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.

KW - Blind source separation

KW - Characteristic function

KW - Hessian matrix

KW - Joint diagonalization

KW - Noncircularity

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JO - Circuits, Systems, and Signal Processing

JF - Circuits, Systems, and Signal Processing

IS - 2

ER -

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

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