Diffusion maximum correntropy criterion algorithms for robust distributed estimation

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• 作者：Wentao Ma^aAuthor Vitae ; Badong Chen^b ; ^{chenbd@mail.xjtu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Jiandong Duan^aAuthor Vitae ; Haiquan Zhao^cAuthor Vitae
• 关键词：Correntropy ; Maximum correntropy criterion ; Diffusion ; Robust distributed adaptive estimation
• 刊名：Digital Signal Processing
• 出版年：2016
• 出版时间：November 2016
• 年：2016
• 卷：58
• 期：Complete
• 页码：10-19
• 全文大小：717 K

文摘

Robust diffusion adaptive estimation algorithms based on the maximum correntropy criterion (MCC), including adapt then combine MCC and combine then adapt MCC, are developed to deal with the distributed estimation over network in impulsive (long-tailed) noise environments. The cost functions used in distributed estimation are in general based on the mean square error (MSE) criterion, which is desirable when the measurement noise is Gaussian. In non-Gaussian situations, especially for the impulsive-noise case, MCC based methods may achieve much better performance than the MSE methods as they take into account higher order statistics of error distribution. The proposed methods can also outperform the robust diffusion least mean p-power (DLMP) and diffusion minimum error entropy (DMEE) algorithms. The mean and mean square convergence analysis of the new algorithms are also carried out.