Signal Processing, Volume (182), Year (2020-12)

Title : ( Diversity-based diffusion robust RLS using adaptive forgetting factor )

Authors: Alireza Naeimi Sadigh , Hadi Sadoghi Yazdi , Ahad Harati ,

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Abstract

In this study, we propose a diffusion robust recursive least squares (D-R2LS) algorithm over adaptive networks. Instead of conventional mean square error cost function, the suggested method is derived from the maximum correntropy criterion (MCC) cost function, being more suitable for non-Gaussian noise. Furthermore, to improve tracking ability when encountering sudden changes in unknown systems in non-stationary environments, a diversity-based extension of D-R2LS is developed by adaptive forgetting factor for each node. Also, to conduct performance analysis, we employ a half-quadratic optimization to approximate our model iteratively by a quadratic problem. The mean, mean-square convergence and stability of the D-R2LS are discussed theoretically. The simulation results show that the proposed methods outperform the other robust algorithms and enhance tracking quality in the presence of non-Gaussian noise in the stationary and non-stationary environments.

Keywords

, Diffusion robust recursive least squares; Half, quadratic optimization; Diversity; Adaptive forgetting factor; Performance analysis.
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@article{paperid:1082926,
author = {Naeimi Sadigh, Alireza and Sadoghi Yazdi, Hadi and Harati, Ahad},
title = {Diversity-based diffusion robust RLS using adaptive forgetting factor},
journal = {Signal Processing},
year = {2020},
volume = {182},
month = {December},
issn = {0165-1684},
keywords = {Diffusion robust recursive least squares; Half-quadratic optimization; Diversity; Adaptive forgetting factor; Performance analysis.},
}

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%0 Journal Article
%T Diversity-based diffusion robust RLS using adaptive forgetting factor
%A Naeimi Sadigh, Alireza
%A Sadoghi Yazdi, Hadi
%A Harati, Ahad
%J Signal Processing
%@ 0165-1684
%D 2020

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