Sliding window orthonormal PAST algorithm

Author

Badeau, R. ; Abed-Meraim, K. ; Richard, G. ; David, B.

Author_Institution

Ecole Nat. Superieure des Telecommun., Paris, France

Volume

5

fYear

2003

fDate

6-10 April 2003

Abstract

This paper introduces an orthonormal version of the sliding-window projection approximation subspace tracker (PAST). The new algorithm guarantees the orthonormality of the signal subspace basis at each iteration. Moreover, it has the same complexity as the original PAST algorithm, and like the more computationally demanding natural power (NP) method, it satisfies a global convergence property, and reaches an excellent tracking performance.

Keywords

adaptive filters; convergence of numerical methods; iterative methods; parameter estimation; source separation; tracking filters; adaptive filtering; complexity; global convergence property; iteration; orthonormal PAST algorithm; orthonormality; parameter estimation; projection approximation subspace tracker; signal subspace basis; sliding window; source localization; tracking performance; Adaptive filters; Convergence; Cost function; Covariance matrix; Data mining; Direction of arrival estimation; Frequency estimation; Iterative methods; Parameter estimation; Recursive estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on

ISSN

1520-6149

Print_ISBN

0-7803-7663-3

Type

conf

DOI

10.1109/ICASSP.2003.1199918

Filename

1199918