A Comparison Between Kernel-based Adaptive Filters Including the Epanechnikov Function

Kernel Adaptive Filtering is an effective solution for nonlinear channel equalization, offering remarkable results in scenarios where linear filters often fail. In this context, the Kernel Maximum Correntropy (KMC) is an efficient and resilient technique. In most cases, the Gaussian kernel is used to calculate correntropy. In this article, we propose to use the Epanechnikov kernel to estimate correntropy and analyze its performance. The filter performance is compared to the KMC with Gaussian kernel and also to the Kernel Least-Mean-Square algorithm.