Title :
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
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;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on
Conference_Location :
Salt Lake City, UT
Print_ISBN :
0-7803-7041-4
DOI :
10.1109/ICASSP.2001.940680
