A Robust Diffusion Estimation Algorithm with Self-Adjusting Step-Size in WSNsReportar como inadecuado


A Robust Diffusion Estimation Algorithm with Self-Adjusting Step-Size in WSNs


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1

College of Electronic and Information Engineering, School of Mathematics and Statistics, Southwest University, and Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing 400715, China

2

College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China



Current address: Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, No.2, Tiansheng Road, Beibei District, Chongqing 400715, China





*

Author to whom correspondence should be addressed.



Abstract In wireless sensor networks WSNs, each sensor node can estimate the global parameter from the local data in a distributed manner. This paper proposed a robust diffusion estimation algorithm based on a minimum error entropy criterion with a self-adjusting step-size, which are referred to as the diffusion MEE-SAS DMEE-SAS algorithm. The DMEE-SAS algorithm has a fast speed of convergence and is robust against non-Gaussian noise in the measurements. The detailed performance analysis of the DMEE-SAS algorithm is performed. By combining the DMEE-SAS algorithm with the diffusion minimum error entropy DMEE algorithm, an Improving DMEE-SAS algorithm is proposed for a non-stationary environment where tracking is very important. The Improving DMEE-SAS algorithm can avoid insensitivity of the DMEE-SAS algorithm due to the small effective step-size near the optimal estimator and obtain a fast convergence speed. Numerical simulations are given to verify the effectiveness and advantages of these proposed algorithms. View Full-Text

Keywords: robust diffusion estimation; self-adjusting step-size; non-Gaussian noise; wireless sensor networks robust diffusion estimation; self-adjusting step-size; non-Gaussian noise; wireless sensor networks





Autor: Xiaodan Shao 1,2, Feng Chen 1,†,* , Qing Ye 2 and Shukai Duan 2

Fuente: http://mdpi.com/



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