The blind deconvolution of the multi-channel based on the higher order statistics

Janghoon Yang; Chrysostomos L. Nikias

doi:10.1109/ACSSC.2000.910752

The blind deconvolution of the multi-channel based on the higher order statistics

Janghoon Yang
, Chrysostomos L. Nikias

Division of Engineering and Science (Abington)

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

1 Scopus citations

Abstract

We have proposed a source separation algorithm of the convolutive mixtures based on the maximization of the auto-kurtosis and minimization of the cross-kurtosis with the constraint on the ouput power. As an iterative method, we suggest a nontrivial extension of the generalized eigenvector algorithm for blind equalization(GEnEVA) to the blind deconvolution of the multi-input multi-output (MIMO) systems. The application of the proposed algorithm on the 64-QAM signal separations shows that it can achieve the excellent performance and it is robust to the broad range of the noise level.

Original language	English (US)
Pages (from-to)	1192-1196
Number of pages	5
Journal	Conference Record of the Asilomar Conference on Signals, Systems and Computers
Volume	2
DOIs	https://doi.org/10.1109/ACSSC.2000.910752
State	Published - 2000

All Science Journal Classification (ASJC) codes

Signal Processing
Computer Networks and Communications

Access to Document

10.1109/ACSSC.2000.910752

Cite this

@article{c95577389670495e810863e19183553a,

title = "The blind deconvolution of the multi-channel based on the higher order statistics",

abstract = "We have proposed a source separation algorithm of the convolutive mixtures based on the maximization of the auto-kurtosis and minimization of the cross-kurtosis with the constraint on the ouput power. As an iterative method, we suggest a nontrivial extension of the generalized eigenvector algorithm for blind equalization(GEnEVA) to the blind deconvolution of the multi-input multi-output (MIMO) systems. The application of the proposed algorithm on the 64-QAM signal separations shows that it can achieve the excellent performance and it is robust to the broad range of the noise level.",

author = "Janghoon Yang and Nikias, \{Chrysostomos L.\}",

year = "2000",

doi = "10.1109/ACSSC.2000.910752",

language = "English (US)",

volume = "2",

pages = "1192--1196",

journal = "Conference Record of the Asilomar Conference on Signals, Systems and Computers",

issn = "1058-6393",

publisher = "IEEE Computer Society",

}

TY - JOUR

T1 - The blind deconvolution of the multi-channel based on the higher order statistics

AU - Yang, Janghoon

AU - Nikias, Chrysostomos L.

PY - 2000

Y1 - 2000

N2 - We have proposed a source separation algorithm of the convolutive mixtures based on the maximization of the auto-kurtosis and minimization of the cross-kurtosis with the constraint on the ouput power. As an iterative method, we suggest a nontrivial extension of the generalized eigenvector algorithm for blind equalization(GEnEVA) to the blind deconvolution of the multi-input multi-output (MIMO) systems. The application of the proposed algorithm on the 64-QAM signal separations shows that it can achieve the excellent performance and it is robust to the broad range of the noise level.

AB - We have proposed a source separation algorithm of the convolutive mixtures based on the maximization of the auto-kurtosis and minimization of the cross-kurtosis with the constraint on the ouput power. As an iterative method, we suggest a nontrivial extension of the generalized eigenvector algorithm for blind equalization(GEnEVA) to the blind deconvolution of the multi-input multi-output (MIMO) systems. The application of the proposed algorithm on the 64-QAM signal separations shows that it can achieve the excellent performance and it is robust to the broad range of the noise level.

UR - https://www.scopus.com/pages/publications/0034443917

UR - https://www.scopus.com/pages/publications/0034443917#tab=citedBy

U2 - 10.1109/ACSSC.2000.910752

DO - 10.1109/ACSSC.2000.910752

M3 - Article

AN - SCOPUS:0034443917

SN - 1058-6393

VL - 2

SP - 1192

EP - 1196

JO - Conference Record of the Asilomar Conference on Signals, Systems and Computers

JF - Conference Record of the Asilomar Conference on Signals, Systems and Computers

ER -

The blind deconvolution of the multi-channel based on the higher order statistics

Abstract

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this