Simplification of stochastic fastest NLMS algorithm

Author

Fukumoto, Masahiro ; Kubota, Hajime ; Tsuji, Satoshi

Author_Institution

Kochi Univ. of Technol., Japan

Volume

3

fYear

1999

fDate

36342

Firstpage

158

Abstract

The behavior of the normalized LMS (NLMS) algorithm is unstable when all elements of an input state vector are very small. This property is caused by the division in the procedure of the NLMS algorithm. It has been taken as one of the countermeasures against the above problem that coefficients of an adaptive filter are not updated when a norm of an input state vector is smaller than a threshold. The guarantee value (least upper bound of the estimation error) and the stochastic fastest convergence step gain with interruption of coefficients update have been shown. In this paper, we show the simplification of the above method. As a result, the proposed algorithm has no need for statistics which are difficult to obtain under normal conditions

Keywords

adaptive filters; convergence of numerical methods; filtering theory; least mean squares methods; stochastic processes; adaptive filter; coefficients update; estimation error; input state vector norm; least upper bound; normalized LMS algorithm; stochastic fastest NLMS algorithm; stochastic fastest convergence step gain; Adaptive filters; Convergence; Error correction; Estimation error; Least squares approximation; Signal to noise ratio; Statistics; Stochastic processes; Stochastic resonance; Upper bound;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1999. ISCAS '99. Proceedings of the 1999 IEEE International Symposium on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-5471-0

Type

conf

DOI

10.1109/ISCAS.1999.778809

Filename

778809