Result Details
Optimal Step-Size LMS Algorithm Using Exponentially Averaged Gradient Vector
Smékal Zdeněk, prof. Ing., CSc., UTKO (FEEC)
Koula Ivan, Ing., UTKO (FEEC)
This letter proposes a new algorithm, which uses an optimal step-size (OSS) weight-adjustment scheme. This strategy leads to a better convergence rate and misadjustment in environments with sudden changes of parameters and for colored input data. The computational complexity is comparable with the well-known RLS. The performance of the novel approach is verified by simulations under system identification scenario and compared with that of the NLMS and RLS algorithms. The strategy uses averaged values of the correlation matrix and the cross-correlation vector. Experimental results for car-interior echo cancelation are presented including analysis of converegnce rate and misadjustment.
adaptive filter, least mean square, recursive least squares, convergence rate, computational complexity, misadjustment, optimal step-size, exponential averaging
@inproceedings{BUT15142,
author="Vladimír {Malenovský} and Zdeněk {Smékal} and Ivan {Koula}",
title="Optimal Step-Size LMS Algorithm Using Exponentially Averaged Gradient Vector",
booktitle="Proceedings of the Intl. Conference EUROCON 2005",
year="2005",
series="SVAZEK: R23 SIGNAL PROCESSING",
pages="4",
publisher="Belgrade, Serbia and Montenegro",
address="Belgrade, Serbia and Montenegro",
isbn="1-4244-0050-3"
}