  Distributed estimation over networks draws much attraction in recent years.
In many situations, due to imperfect information communication among nodes, the
performance of traditional diffusion adaptive algorithms such as the diffusion
LMS (DLMS) may degrade. To deal with this problem, several modified DLMS
algorithms have been proposed. However, these DLMS based algorithms still
suffer from biased estimation and are not robust to impulsive link noise. In
this paper, we focus on improving the performance of diffusion adaptation with
noisy links from two aspects: accuracy and robustness. A new algorithm called
diffusion maximum total correntropy (DMTC) is proposed. The new algorithm is
theoretically unbiased in Gaussian noise, and can efficiently handle the link
noises in the presence of large outliers. The adaptive combination rule is
applied to further improve the performance. The stability analysis of the
proposed algorithm is given. Simulation results show that the DMTC algorithm
can achieve good performance in both Gaussian and non-Gaussian noise
environments.
