Robust proportionate adaptive filter based on maximum correntropy criterion for sparse system identification in impulsive noise environments

Proportionate type adaptive filtering (PtAF) algorithms have been successfully applied for sparse system identification. The major drawback of the traditional PtAF based on the mean square error (MSE) criterion is poor robustness in the presence of abrupt changes because the MSE is valid and rational under Gaussian assumption. However, this assumption is not satisfied in most real-world applications. To improve its robustness under non-Gaussian environments, we incorporate the maximum correntropy criterion (MCC) into the update equation of the PtAF to develop proportionate MCC (PMCC) algorithm. The mean and mean square convergence performance analysis are also performed. Simulation results in sparse system identification and echo cancellation applications are presented, which demonstrate that the proposed PMCC exhibits outstanding performance under the impulsive noise environments.