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Title: A robust mixed-norm adaptive filter algorithm
Authors: Chambers, Jonathon
Avlonitis, Apostolos
Keywords: Gaussian noise
Adaptive filters
Adaptive signal processing
Error analysis
Filtering theory
Least mean squares methods
Parameter estimation
Issue Date: 1997
Publisher: © IEEE
Citation: CHAMBERS, J. and AVLONITIS, A., 1997. A robust mixed-norm adaptive filter algorithm. IEEE Signal Processing Letters, 4 (2), pp. 46 - 48
Abstract: We propose a new member of the family of mixed-norm stochastic gradient adaptive filter algorithms for system identification applications based upon a convex function of the error norms that underlie the least mean square (LMS) and least absolute difference (LAD) algorithms. A scalar parameter controls the mixture and relates, approximately, to the probability that the instantaneous desired response of the adaptive filter does not contain significant impulsive noise. The parameter is calculated with the complementary error function and a robust estimate of the standard deviation of the desired response. The performance of the proposed algorithm is demonstrated in a system identification simulation with impulsive and Gaussian measurement noise
Description: This article was published in the journal, IEEE Signal Processing Letters [© IEEE]. It is also available from: http://ieeexplore.ieee.org/servlet/opac?punumber=97. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Version: Published
DOI: 10.1109/97.554469
URI: https://dspace.lboro.ac.uk/2134/5849
ISSN: 1070-9908
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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