Adaptive Filtering under the Maximum Correntropy Criterion with Variable Center

Recently, an extended version of correntropy, whose center can locate at any position has been proposed and applied in a new optimization criterion called maximum correntropy criterion with variable center (MCC-VC). In order to optimize the performance of adaptive filtering in non-Gaussian and non-zero mean noise environments, in this paper, we propose a stochastic gradient adaptive filtering algorithm for online learning based on MCC-VC and analyze its stability and convergence performance. Moreover, we also extend an online learning approach to estimate the kernel width and the center location, in which two parameters have a great influence on the accuracy of the algorithm. The simulation results of the online learning model have verified the superiority and robustness of the new method.