Adaptable Nonlinearity for Complex Maximization of Nongaussianity and a Fixed-Point Algorithm

Author

Novey, Mike ; Adali, Tiilay

Author_Institution

Univ. of Maryland Baltimore County, Baltimore, MD, USA

fYear

2006

fDate

6-8 Sept. 2006

Firstpage

79

Lastpage

84

Abstract

Complex maximization of nonGaussianity (CMN) has been shown to provide reliable separation of both circular and non-circular sources using a class of complex functions in the non-linearity. In this paper, we derive a fixed-point algorithm for blind separation of noncircular sources using CMN. We also introduce the adaptive CMN (A-CMN) algorithm that provides significant performance improvement by adapting the nonlinearity to the source distribution. The ability of A-CMN to adapt to a wide range of source statistics is demonstrated by simulation results.

Keywords

adaptive signal processing; blind source separation; optimisation; statistical analysis; adaptable nonlinearity; adaptive CMN algorithm; fixed-point algorithm; nonGaussianity complex maximization; noncircular sources blind separation; source statistics; Adaptive algorithm; Covariance matrix; Entropy; Independent component analysis; Maximum likelihood estimation; Newton method; Parameter estimation; Random variables; Shape; Statistical distributions;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning for Signal Processing, 2006. Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on

Conference_Location

Arlington, VA

ISSN

1551-2541

Print_ISBN

1-4244-0656-0

Electronic_ISBN

1551-2541

Type

conf

DOI

10.1109/MLSP.2006.275526

Filename

4053625