A Robust Generalized Proportionate Diffusion LMS Algorithm for Distributed Estimation

This brief paper proposes a robust generalized proportionate diffusion Least Mean Square (LMS) algorithm for distributed estimation of a parameter vector in a network. The contribution of this brief is twofold. First, we generalize the concept of proportionate diffusion LMS by letting the gain matri...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems. II, Express briefs Vol. 68; no. 4; pp. 1552 - 1556
Main Authors: Zayyani, Hadi, Javaheri, Amirhossein
Format: Journal Article
Language:English
Published: New York IEEE 01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Circuits and systems
Cost function
Diffusion
diffusion LMS
Distributed estimation
disturbance
Estimation
impulsive noise
Indexes
Parameter estimation
proportionate
robust
Robustness
Sensors
Signal processing algorithms
ISSN:1549-7747, 1558-3791
Online Access:Get full text
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Summary:This brief paper proposes a robust generalized proportionate diffusion Least Mean Square (LMS) algorithm for distributed estimation of a parameter vector in a network. The contribution of this brief is twofold. First, we generalize the concept of proportionate diffusion LMS by letting the gain matrix to be non-diagonal instead of being a diagonal matrix. Second, to achieve robustness to impulsive noise while simultaneously maintaining a fast convergence property, we use a combination of Mean Square Deviation (MSD) and disturbance incurred in the adaptation step as the objective cost function. By simplifying and optimizing the proposed cost function, a closed form formula is obtained for the gain matrix in the general non-diagonal case. Simulation results demonstrate the efficiency of the proposed method in comparison to some other state-of-the-art algorithms.
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ISSN:1549-7747
1558-3791
DOI:10.1109/TCSII.2020.3029780