Diffusion bias-compensated recursive maximum correntropy criterion algorithm with noisy input

In recent years, the distributed estimation problem based on the diffusion strategy has received more and more attention, where the node filters cooperate with other in-network node filters to estimate the unknown parameter vector. In this paper, we propose a Diffusion Bias Compensated Recursive Maximum Correntropy Criterion (DBCRMCC) algorithm based on the idea of distributed estimation for adaptive filtering in network containing input noise and non-Gaussian output noise. The Recursive Maximum Correntropy Criterion (RMCC) is an adaptive filtering algorithm with Maximum Correntropy Criterion (MCC) as the cost function which is robust to large outliers. Considering that the estimates directly obtained by the RMCC algorithm are biased when the input is disturbed by noise, this paper proposes the DBCRMCC algorithm under some reasonable assumptions by using the principle of unbiased estimation and combining it with a diffusion strategy. Through bias-compensation for biased estimates and cooperation among node filters, the asymptotic unbiased estimates of the unknown parameters can be obtained. The simulation results show that the proposed DBCRMCC algorithm has acceptable convergence speed and estimation accuracy in the environment where the input signal is noisy and the output noise is non-Gaussian.