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