Shao, X.; Chen, F.; Ye, Q.; Duan, S. A Robust Diffusion Estimation Algorithm with Self-Adjusting Step-Size in WSNs. Sensors2017, 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.
Shao, X.; Chen, F.; Ye, Q.; Duan, S. A Robust Diffusion Estimation Algorithm with Self-Adjusting Step-Size in WSNs. Sensors2017, 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.
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.
Computer Science and Mathematics, Computer Science
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