Adaptive forgetting factor recursive least squares adaptive threshold nonlinear algorithm (RFF-RLS-ATNA) for identification of nonstationary systems

Author

Koike, Shinichi

Author_Institution

NEC Corp., Tokyo, Japan

Volume

6

fYear

2003

fDate

6-10 April 2003

Abstract

The recursive least squares (RLS) adaptive algorithm is combined with the "adaptive threshold nonlinear algorithm" (ATNA) proposed by the author (Koike, S., IEEE Trans. Sig. Processing, vol.45, p.2391-5, 1997), to derive RLS-ATNA, resulting in improvement of the convergence rate of the ATNA that offers robust adaptive filters in impulse noise environments. For application of the RLS-ATNA to identification of random-walk modeled nonstationary systems, an adaptive forgetting factor (AFF) control algorithm is proposed that further improves the tracking performance in the steady state. Through analysis and experiments, the effectiveness of the AFF-RLS-ATNA is demonstrated. Fairly good agreement between the simulation and the theoretically calculated convergence validates the analysis.

Keywords

adaptive filters; burst noise; convergence of numerical methods; identification; impulse noise; nonlinear systems; recursive functions; tracking; adaptive forgetting factor; adaptive threshold nonlinear algorithm; burst noise; convergence; impulse noise; nonstationary systems identification; recursive least squares; robust adaptive filters; tracking performance; Adaptive algorithm; Adaptive control; Adaptive filters; Adaptive systems; Convergence; Least squares methods; Noise robustness; Programmable control; Resonance light scattering; Working environment noise;

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.2003.1201757

Filename

1201757