Improved variable forgetting factor recursive least square algorithm

Author

Albu, Felix

Author_Institution

Dept. of Electron. & Telecommun., Valahia Univ. of Targoviste, Targoviste, Romania

fYear

2012

Firstpage

1789

Lastpage

1793

Abstract

In this paper an improved variable forgetting factor recursive least square (IVFF-RLS) algorithm is proposed. The forgetting factor is adjusted according to the square of a time-averaging estimate of the autocorrelation of a priori and a posteriori errors. The proposed algorithm has fast convergence, and robustness against variable background noise, near-end signal variations and echo path change. The simulation results indicate the superior performances of IVFF-RLS when compared to the RLS and VFF-RLS algorithms.

Keywords

adaptive filters; convergence; correlation methods; identification; least squares approximations; recursive estimation; IVFF-RLS algorithm; a posteriori error; a priori error; adaptive filter; autocorrelation; convergence; echo path change; improved variable forgetting factor recursive least square algorithm; near-end signal variation; robustness; time-averaging estimate; variable background noise; Adaptive filters; Convergence; Noise measurement; Signal processing algorithms; Signal to noise ratio; Speech; System identification; adaptive control; echo cancellation; recursive least squares; system identification; variable forgetting factor;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Automation Robotics & Vision (ICARCV), 2012 12th International Conference on

Conference_Location

Guangzhou

Print_ISBN

978-1-4673-1871-6

Electronic_ISBN

978-1-4673-1870-9

Type

conf

DOI

10.1109/ICARCV.2012.6485421

Filename

6485421