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  4. A diffusion LMS strategy for parameter estimation in noisy regressor applications
 
conference paper

A diffusion LMS strategy for parameter estimation in noisy regressor applications

Abdolee, Reza
•
Champagne, Benoit
•
Sayed, Ali H.  
2012
Proceedings of the 20th European Signal Processing Conference (EUSIPCO))
20th European Signal Processing Conference (EUSIPCO)

We study distributed least-mean square (LMS) estimation problems over adaptive networks, where nodes cooperatively work to estimate and track common parameters of an unknown system. We consider a scenario where the input and output response signals of the unknown system are both contaminated by measurement noise. In this case, if standard distributed estimation is performed without considering the effect of regression noise, then the resulting parameter estimates will be biased. To resolve this problem, we propose a distributed LMS algorithm that achieves asymptotically unbiased estimates via diffusion adaptation. We analyze the performance of the proposed algorithm and provide computer experiments to illustrate its behavior.

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Type
conference paper
Author(s)
Abdolee, Reza
Champagne, Benoit
Sayed, Ali H.  
Date Issued

2012

Published in
Proceedings of the 20th European Signal Processing Conference (EUSIPCO))
ISBN of the book

978-1-4673-1068-0

Start page

749

End page

753

Editorial or Peer reviewed

REVIEWED

Written at

OTHER

EPFL units
ASL  
Event nameEvent placeEvent date
20th European Signal Processing Conference (EUSIPCO)

Bucharest, Romania

August 27-31, 2012

Available on Infoscience
December 19, 2017
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/143299
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