PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A normalized adaptation scheme for the convex combination of two adaptive filters
Luis A. Azpicueta-Ruiz, Aníbal R. Figueiras-Vidal and Jerónimo Arenas-Garcia
In: IEEE Intl. Conference on Acoustics, Speech and Signal Processing (ICASSP'08), Las Vegas(2008).

Abstract

Adaptive filtering schemes are subject to different tradeoffs regarding their steady-state misadjustment, speed of convergence and tracking performance. To alleviate these compromises, a new approach has recently been proposed, in which two filters with complementary capabilities adaptively mix their outputs to get an overall filter of improved performance. Following this approach, in this paper we propose a new normalized rule for adapting the mixing parameter that controls the combination. The new update rule preserves the good features of the standard scheme and is more robust to changes in the filtering scenario, for instance when the signal to noise ratio (SNR) is time varying. The benefits of the normalized scheme are illustrated analytically and with a number of experiments in both stationary and tracking situations.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:5052
Deposited By:Jerónimo Arenas-Garcia
Deposited On:24 March 2009