Convergence analysis of the NLMS algorithm with M-independent inputs

Author

Scalart, Pascal

Author_Institution

France Telecom R&D, Lannion, France

Volume

6

fYear

2001

fDate

2001

Firstpage

3849

Abstract

In most adaptive identification applications, a finite impulse response (FIR) filter is employed with coefficients that are computed using the normalized least mean square (NLMS) algorithm. The convergence behavior of the NLMS algorithm is analyzed using a simple model of the input signal vectors. Explicit expressions of the learning curve and misadjustment are derived and compared with those previously established for the NLMS algorithm. Comparisons between theoretical and experimental results are given to validate our approach

Keywords

FIR filters; adaptive filters; adaptive signal processing; convergence of numerical methods; filtering theory; identification; least mean squares methods; FIR filter; NLMS algorithm; adaptive identification; convergence analysis; finite impulse response filter; input signal vectors; learning curve; normalized least mean square algorithm; Adaptive filters; Algorithm design and analysis; Convergence; Finite impulse response filter; Least squares approximation; Random variables; Signal analysis; Signal processing; Telecommunications; Wiener filter;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on

Conference_Location

Salt Lake City, UT

ISSN

1520-6149

Print_ISBN

0-7803-7041-4

Type

conf

DOI

10.1109/ICASSP.2001.940683

Filename

940683