Diffusion Correntropy Subband Adaptive Filtering (SAF) Algorithm over Distributed Smart Dust Networks

Abstract

The diffusion subband adaptive filtering (DSAF) algorithm has attracted much attention in recent years due to its decorrelation ability for colored input signals. In this paper, a modified DSAF algorithm using the symmetry maximum correntropy criterion (MCC) with individual weighting factors is proposed and discussed to combat impulsive noise, which is denoted as the MCC-DSAF algorithm. During the iterations, the negative exponent in the Gaussian kernel of the MCC-DSAF eliminates the interference of outliers to provide a robust performance in non-Gaussian noise environments. Moreover, in order to enhance the convergence for sparse system identifications, a variant of MCC-DSAF named as improved proportionate MCC-DSAF (MCC-IPDSAF) is presented and investigated, which provides a dynamic gain assignment matrix in the MCC-DSAF to adjust the weighted values of each coefficient. Simulation results verify that the newly presented MCC-DSAF and MCC-IPDSAF algorithms are superior to the popular DSAF algorithms.