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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Vydáno v:IEEE transactions on circuits and systems. II, Express briefs Ročník 68; číslo 4; s. 1552 - 1556
Hlavní autoři: Zayyani, Hadi, Javaheri, Amirhossein
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
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Abstract 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.
AbstractList 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.
Author Javaheri, Amirhossein
Zayyani, Hadi
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SubjectTerms 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
Title A Robust Generalized Proportionate Diffusion LMS Algorithm for Distributed Estimation
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