Iterative Wiener design of adaptation laws with constant gains

Author

Ahlén, Anders ; Sternad, Mikael ; Lindbom, Lars

Author_Institution

Uppsala Univ., Sweden

Volume

6

fYear

2001

fDate

2001

Firstpage

3861

Abstract

We present a method for optimizing adaptation laws that are generalizations of the LMS algorithm. Time-varying parameters of linear regression models are estimated in situations where the regressors are stationary or have slowly time-varying properties. The parameter variations are modeled as ARIMA processes and the aim is to use such prior information to provide high-performance filtering, prediction or fixed lag smoothing estimates for arbitrary lags. The method is based on a novel signal transformation that recasts the algorithm design problem into a Wiener design

Keywords

Wiener filters; adaptive estimation; adaptive filters; autoregressive moving average processes; iterative methods; least mean squares methods; parameter estimation; statistical analysis; time-varying systems; ARIMA processes; LMS algorithm; Wiener design; adaptation laws; arbitrary lags; constant gains; fixed lag smoothing; high-performance filtering; iterative Wiener design; linear regression models; prediction; signal transformation; stationary regressors; time-varying parameter estimation; Algorithm design and analysis; Information filtering; Information filters; Iterative algorithms; Least squares approximation; Linear regression; Optimization methods; Predictive models; Signal design; Smoothing methods;

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.2001.940686

Filename

940686