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^*

^*Corresponding author for this work

School of Integrated Circuits and Electronics

Beijing Institute of Technology

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

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.

Original language	English
Pages (from-to)	695-705
Number of pages	11
Journal	Circuits, Systems, and Signal Processing
Volume	34
Issue number	2
DOIs	https://doi.org/10.1007/s00034-014-9867-5
Publication status	Published - Feb 2015

Keywords

Blind source separation
Characteristic function
Hessian matrix
Joint diagonalization
Noncircularity

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10.1007/s00034-014-9867-5

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title = "Blind Separation of Noncircular Sources Via Approximate Joint Diagonalization of Augmented Charrelation Matrices",

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",

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AU - Gou, Xiaoming

AU - Liu, Zhiwen

AU - Ma, Jingyan

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

UR - https://www.scopus.com/pages/publications/84922222914

U2 - 10.1007/s00034-014-9867-5

DO - 10.1007/s00034-014-9867-5

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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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Keywords

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