Stochastic analysis of the LMS algorithm for non-stationary white Gaussian inputs

Author

Bershad, Neil J. ; Bermudez, José C M

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., Univ. of California Irvine, Irvine, CA, USA

fYear

2011

fDate

28-30 June 2011

Firstpage

57

Lastpage

60

Abstract

This paper studies the stochastic behavior of the LMS algorithm for a system identification framework when the input signal is a non-stationary white Gaussian process. The unknown system is modeled by the standard random walk model. An approximate theory is developed which is based upon the instantaneous average power in the adaptive filter taps. The stability of the algorithm is investigated using this model. Monte Carlo simulations of the algorithm provides strong support for the theoretical approximation.

Keywords

Monte Carlo methods; adaptive filters; stochastic games; LMS algorithm; Monte Carlo simulations; adaptive filter taps; input signals; instantaneous average power; nonstationary white Gaussian inputs; standard random walk model; stochastic analysis; system identification framework; Adaptation models; Algorithm design and analysis; Analytical models; Approximation algorithms; Least squares approximation; Signal processing algorithms; Adaptive filters; Analysis; LMS Algorithm; stochastic algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistical Signal Processing Workshop (SSP), 2011 IEEE

Conference_Location

Nice

ISSN

pending

Print_ISBN

978-1-4577-0569-4

Type

conf

DOI

10.1109/SSP.2011.5967764

Filename

5967764