A new family of approximate QR-LS algorithms for adaptive filtering

Author

Zhou, Y. ; Chan, S.C.

Author_Institution

Dept. of Electr. & Electron. Eng., Hong Kong Univ.

fYear

2005

fDate

17-20 July 2005

Firstpage

71

Lastpage

76

Abstract

This paper proposes a new family of approximate QR-based least squares (LS) adaptive filtering algorithms called p-TA-QR-LS algorithms. It extends the TA-QR-LS algorithm by retaining different number of diagonal plus off-diagonals (denoted by an integer p) of the triangular factor of the augmented data matrix. For p=1 and N it reduces respectively to the TA-QR-LS and the QR-RLS algorithms. It not only provides a link between the QR-LMS-type and the QR-RLS algorithms through a well-structured family of algorithms, but also offers flexible complexity-performance tradeoffs in practical implementation. These results are verified by computer simulation and the mean convergence of the algorithms is also analyzed

Keywords

adaptive filters; convergence of numerical methods; least squares approximations; matrix algebra; adaptive filtering; approximate QR-LS algorithm; data matrix augmentation; least square algorithm; mean convergence; Adaptive filters; Algorithm design and analysis; Application software; Arithmetic; Communication system control; Computer simulation; Convergence; Covariance matrix; Filtering algorithms; Least squares approximation;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistical Signal Processing, 2005 IEEE/SP 13th Workshop on

Conference_Location

Novosibirsk

Print_ISBN

0-7803-9403-8

Type

conf

DOI

10.1109/SSP.2005.1628567

Filename

1628567