Open AccessVariable step‐size non‐negative normalised least‐mean‐square‐type algorithm
Author(s) -
Jung Sang Mok,
Seo JiHye,
Park PooGyeon
Publication year - 2015
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2014.0122
Subject(s) - constraint (computer aided design) , algorithm , convergence (economics) , least mean squares filter , mathematics , mean squared error , adaptive filter , variable (mathematics) , computer science , mathematical optimization , control theory (sociology) , statistics , artificial intelligence , mathematical analysis , geometry , economics , economic growth , control (management)
This paper proposes a fast and precise adaptive filtering algorithm for online estimation under a non‐negativity constraint. A novel variable step‐size (VSS) non‐negative normalised least‐mean‐square (NLMS)‐type algorithm based on the mean‐square deviation (MSD) analysis with a non‐negativity constraint is derived. The NLMS‐type algorithm under the non‐negativity constraint is derived by using the gradient descent of the given cost function and the fixed‐point iteration method. Furthermore, the VSS derived by minimising the MSD yields improvement of the filter performance in the aspects of the convergence rate and the steady‐state estimation error. Simulation results show that the proposed algorithm outperforms existing algorithms.