We introduce a new robust algorithm that is insensitive to impulsive noise (IN) for distributed estimation problem over adaptive networks. Motivated by the fact that each node can access to multiple spatial data, we propose to discard IN-contaminated data. Under the assumption that IN is successfully detected, we propose a cost function that considers only the uncontaminated data. The derived algorithm is the ATC diffusion LMS algorithm that has variable weighting coefficients depending on IN detection, which leads both to insensitivity to IN and to good estimation performance. A method to detect IN is also presented. Simulation results show that the proposed algorithm has good estimation performance in an environment that is subject to IN, and outperforms the conventional robust algorithms. HighlightsWe propose a new robust algorithm for distributed estimation over adaptive networks.We propose a cost function that considers only the data without impulsive noise.Weighting coefficients that vary depending on impulsive noise occurrence are derived.A method to detect impulsive noise is also presented.The proposed algorithm outperforms the conventional robust algorithms.
['Do-Chang Ahn', 'Jae-Woo Lee', 'Seung-Jun Shin', 'Woo-Jin Song']
A new robust variable weighting coefficients diffusion LMS algorithm