Preprint Article Version 1 This version is not peer-reviewed

A Robust Diffusion Estimation Algorithm with Self-adjusting Step-size in WSNs

Version 1 : Received: 27 February 2017 / Approved: 28 February 2017 / Online: 28 February 2017 (12:38:25 CET)

A peer-reviewed article of this Preprint also exists.

Shao, X.; Chen, F.; Ye, Q.; Duan, S. A Robust Diffusion Estimation Algorithm with Self-Adjusting Step-Size in WSNs. Sensors 2017, 17, 824. Shao, X.; Chen, F.; Ye, Q.; Duan, S. A Robust Diffusion Estimation Algorithm with Self-Adjusting Step-Size in WSNs. Sensors 2017, 17, 824.

Journal reference: Sensors 2017, 17, 824
DOI: 10.3390/s17040824

Abstract

In wireless sensor networks (WSNs), each sensor node can estimate the global parameter from the local data in distributed manner. This paper proposed a robust diffusion estimation algorithm based on minimum error entropy criterion with self-adjusting step-size, which are referred to as diffusion MEE-SAS (DMEE-SAS) algorithm. The DMEE-SAS algorithm has 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 with diffusion minimum error entropy (DMEE) algorithms, an Improving DMEE-SAS algorithm is proposed, in 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.

Subject Areas

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

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