Robust diffusion maximum correntropy criterion algorithm for distributed network estimation

Robust diffusion algorithms based on the maximum correntropy criterion(MCC) are developed to address the distributed networks estimation issue in impulsive(long-tailed) noise environments. The cost functions used in distributed network estimation are in general based on the mean square error (MSE) criterion, which is optimal only when the measurement noise is Gaussian. In non-Gaussian situations, such as the impulsive-noise case, MCC based method may achieve a much better performance than the MSE methods since it takes 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.