A stochastic model for the convergence behavior of the affine projection algorithm for Gaussian inputs

Author

de Almeida, S.I.M. ; Bermudez, José C M ; Bershad, Neil J. ; Costa, Márcio H.

Author_Institution

Catholic Univ. of Pelotas, Brazil

Volume

6

fYear

2003

fDate

6-10 April 2003

Abstract

The paper presents an analytical model for predicting the stochastic behavior of the affine projection (AP) algorithm. The model is derived for autoregressive (AR) Gaussian inputs and for unity step size (fastest convergence). Deterministic recursive equations are presented for the mean weight and mean square error for a large number of adaptive taps, N, as compared to the algorithm order, P. The model predictions show better agreement between theory and simulations in transient and steady-state than previous models described in the literature. The learning behavior of the AP algorithm is of great interest in applications such as acoustic echo cancellation.

Keywords

Gaussian processes; adaptive signal processing; autoregressive processes; convergence of numerical methods; mean square error methods; recursive functions; acoustic echo cancellation; adaptive taps; affine projection algorithm; autoregressive Gaussian inputs; convergence behavior; deterministic equations; mean square error; mean weight; recursive equations; stochastic model; unity step size; Acoustic applications; Analytical models; Convergence; Echo cancellers; Equations; Mean square error methods; Predictive models; Projection algorithms; Steady-state; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on

ISSN

1520-6149

Print_ISBN

0-7803-7663-3

Type

conf

DOI

10.1109/ICASSP.2003.1201681

Filename

1201681