Distributed Parameter Estimation for Univariate Generalized Gaussian Distribution over Sensor Networks

Generalized Gaussian distribution (GGD) is one of the most prominent and widely used parametric distributions to model the statistical properties of various phenomena. Parameter estimation for these distributions becomes a fundamental problem. However, most of the existing parameter estimation techn...

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Bibliographic Details
Published in:Circuits, systems, and signal processing systems, and signal processing, 2017-03, Vol.36 (3), p.1311-1321
Main Authors: Liang, Chen, Wen, Fuxi, Wang, Zhongmin
Format: Article
Language:English
Subjects:
Algorithms
Circuits
Circuits and Systems
Convergence
Efficiency
Electrical Engineering
Electronics and Microelectronics
Engineering
Instrumentation
Mathematical models
Networks
Normal distribution
Parameter estimation
Sensors
Short Paper
Signal,Image and Speech Processing
Studies
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Summary:Generalized Gaussian distribution (GGD) is one of the most prominent and widely used parametric distributions to model the statistical properties of various phenomena. Parameter estimation for these distributions becomes a fundamental problem. However, most of the existing parameter estimation techniques are centralized. In this paper, we consider distributed parameter estimation for univariate GGD over sensor networks. Parameters among different nodes are estimated cooperatively for the proposed diffusion techniques. Numerical studies are carried out to evaluate the efficiency of the proposed methods, in terms of average root mean square error and convergence rate.
ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-016-0345-0