Subspace Tracking in Colored Noise Based on Oblique Projection

Author

Chen, Minhua ; Wang, Zuoying

Author_Institution

Dept. of Electron. Eng., Tsinghua Univ., Beijing

Volume

3

fYear

2006

fDate

14-19 May 2006

Abstract

Projection approximation subspace tracking (PAST) algorithm gives biased subspace estimation when the received signal is corrupted by colored noise. In this paper, an unbiased version of PAST is proposed for the colored noise scenario. Firstly, a maximum likelihood (ML) and minimum variance unbiased (MVUB) estimator for the clean signal is derived using simultaneous diagonalization and oblique projection. Then, we provide a recursive algorithm, named oblique PAST (obPAST), to track the signal subspace and update the estimator in colored noise. Experimental results show the effectiveness of the obPAST algorithm

Keywords

approximation theory; covariance matrices; maximum likelihood estimation; noise; signal processing; colored noise; covariance matrix; maximum likelihood estimator; minimum variance unbiased estimator; oblique projection; projection approximation subspace tracking; signal subspace; simultaneous diagonalization; Approximation algorithms; Colored noise; Covariance matrix; Eigenvalues and eigenfunctions; Matrix decomposition; Maximum likelihood estimation; Recursive estimation; Signal processing algorithms; Singular value decomposition; White noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on

Conference_Location

Toulouse

ISSN

1520-6149

Print_ISBN

1-4244-0469-X

Type

conf

DOI

10.1109/ICASSP.2006.1660714

Filename

1660714