A stochastic analysis of the affine projection algorithm for Gaussian autoregressive inputs

Author

Bershad, N.J. ; Linebarger, D. ; McLaughlin, S.

Author_Institution

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

Volume

6

fYear

2001

fDate

2001

Firstpage

3837

Abstract

This paper studies the statistical behavior of the affine projection (AP) algorithm for μ=1 for Gaussian autoregressive inputs. This work extends the theoretical results of Rupp (1998) to the numerical evaluation of the MSE learning curves for the adaptive AP weights. The MSE learning behavior of the AP(P+1) algorithm with an AR(Q) input (Q⩽P) is shown to be the same as the NLMS algorithm (μ=1) with a white input with M-P unity eigenvalues and P zero eigenvalues and increased observation noise. Monte Carlo simulations are presented which support the theoretical results

Keywords

Gaussian processes; Monte Carlo methods; autoregressive processes; decorrelation; eigenvalues and eigenfunctions; statistical analysis; AR input; Gaussian autoregressive inputs; MSE learning curves; Monte Carlo simulations; adaptive AP weights; affine projection algorithm; eigenvalues; observation noise; statistical behavior; stochastic analysis; Additive noise; Algorithm design and analysis; Decorrelation; Eigenvalues and eigenfunctions; Least squares approximation; Parameter estimation; Projection algorithms; Random sequences; Signal analysis; Stochastic processes;

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.2001.940680

Filename

940680