Convergence and steady-state properties of the least-mean mixed-norm (LMMN) adaptive algorithm

Author

Tanrikulu, O. ; Chambers, J.A.

Author_Institution

Dept. of Electr. & Electron. Eng., Imperial Coll. of Sci., Technol. & Med., London, UK

Volume

143

Issue

3

fYear

1996

fDate

6/1/1996 12:00:00 AM

Firstpage

137

Lastpage

142

Abstract

Convergence and steady-state analyses of a least-mean mixed-norm adaptive algorithm are presented. This is formed as a convex mixture of the mean-square and the mean-fourth cost functions. The local exponential stability of the algorithm is shown by application of the deterministic averaging analysis and the total stability theorem. A theoretical misadjustment expression is then obtained by using the ordinary-differential-equation method. Simulation studies are presented to support the theoretical findings. The results demonstrate the advantage of mixing error norms in adaptive filtering when the measurement noise is composed of a linear combination of long-tail and short-tail noise distributions

Keywords

adaptive filters; differential equations; error analysis; filtering theory; least mean squares methods; measurement; noise; numerical stability; statistical analysis; adaptive filtering; convergence analysis; convex mixture; deterministic averaging analysis; least mean mixed norm adaptive algorithm; local exponential stability; long tail noise distribution; mean fourth cost function; mean square cost function; measurement noise; misadjustment expression; mixing error norms; ordinary differential equation method; short tail noise distribution; simulation; steady state properties; total stability theorem;

fLanguage

English

Journal_Title

Vision, Image and Signal Processing, IEE Proceedings -

Publisher

iet

ISSN

1350-245X

Type

jour

DOI

10.1049/ip-vis:19960449

Filename

503656