A Variable Regularization Matrix Normalized Subband Adaptive Filter

Author

Ni, Jingen ; Li, Feng

Author_Institution

Dept. of Electron. Eng., Fudan Univ., Shanghai

Volume

16

Issue

2

fYear

2009

Firstpage

105

Lastpage

108

Abstract

The normalized subband adaptive filter (NSAF) proposed by Lee and Gan is promising. However, there exists the conflicting requirement of fast convergence rate and low misadjustment for the NSAF. In this letter, we propose a variable regularization matrix NSAF (VRM-NSAF) to address this problem. The optimal selection of the regularization matrix is derived by the largest decrease of the mean-square deviation (MSD). Simulation results comparing the proposed VRM-NSAF with the original NSAF are presented to show the advantage of this method, including both fast convergence rate and low misadjustment.

Keywords

adaptive filters; convergence of numerical methods; matrix algebra; mean square error methods; VRM-NSAF; convergence rate; matrix normalized subband adaptive filter; mean-square deviation; variable regularization matrix; Adaptive filters; Computational complexity; Convergence; Filtering algorithms; Gallium nitride; Helium; Least squares approximation; Projection algorithms; Robustness; Steady-state; Adaptive filtering; normalized subband adaptive filter (NSAF); variable regularization matrix;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/LSP.2008.2009848

Filename

4745935