Title :
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
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;
Conference_Titel :
Statistical Signal Processing Workshop (SSP), 2011 IEEE
Conference_Location :
Nice
Print_ISBN :
978-1-4577-0569-4
DOI :
10.1109/SSP.2011.5967764
