Micro-statistic LMS filtering

Author

Chen, Shoupu ; Arce, Gonzalo R.

Author_Institution

Dept. of Electr. Eng., Delaware Univ., Newark, DE, USA

Volume

6

fYear

1992

fDate

10-13 May 1992

Firstpage

2613

Abstract

The theory for the class of microstatistic least-mean-square (LMS) filters which requires the adaptation of the statistical characterization of the set of decomposed signals is developed. The authors develop the theoretical framework for adaptive microstatistic filters for applications where the second-order statistics of the threshold signals are not known, or when they may be nonstationary. A multilevel threshold decomposition is used such that real valued stochastic processes can be filtered and the computation complexity of the algorithm can be arbitrary reduced. The superiority of the new adaptive algorithms is shown analytically as well as by way of simulations

Keywords

computational complexity; filtering and prediction theory; least squares approximations; stochastic processes; adaptive microstatistic filters; computation complexity; decomposed signals; micro-statistic LMS filtering; multilevel threshold decomposition; real valued stochastic processes; second-order statistics; statistical characterization; Adaptive filters; Equations; Filtering; Least squares approximation; Nonlinear filters; Random processes; Signal design; Signal processing; Signal resolution; Statistics;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1992. ISCAS '92. Proceedings., 1992 IEEE International Symposium on

Conference_Location

San Diego, CA

Print_ISBN

0-7803-0593-0

Type

conf

DOI

10.1109/ISCAS.1992.230602

Filename

230602