Blind Separation of Noncircular Correlated Sources Using Gaussian Entropy Rate

Author

Li, Xi-Lin ; Adali, Tülay

Author_Institution

Dept. of Comput. Sci. & Electr. En gineering, Univ. of Maryland, Baltimore, MD, USA

Volume

59

Issue

6

fYear

2011

fDate

6/1/2011 12:00:00 AM

Firstpage

2969

Lastpage

2975

Abstract

We introduce a new blind source separation (BSS) algorithm for correlated noncircular sources that uses only second-order statistics and fully takes the correlation structure into account. We propose a parametric entropy rate estimator that uses a widely linear autoregressive (AR) model for the sources, and derive the BSS algorithm by minimizing the mutual information of separated time series. We compare the performance of the new algorithm with competing algorithms and demonstrate its superior separation performance as well as its effectiveness in separation of non-Gaussian sources when the identification conditions are met.

Keywords

Gaussian processes; autoregressive processes; blind source separation; independent component analysis; time series; Gaussian entropy rate stationary Gaussian source; blind source separation algorithm; correlation structure; independent component analysis; linear autoregressive model; mutual information; noncircular correlated source; parametric entropy rate estimator; second-order statistics; time series; Approximation algorithms; Correlation; Cost function; Covariance matrix; Entropy; Joints; Source separation; Blind source separation; complex Gaussian sources; independent component analysis; noncircular sources; second-order statistics;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/TSP.2011.2114653

Filename

5713268