Maximum Versoria Criterion-Based Robust Adaptive Filtering Algorithm

Abstract

Using the generalized Gaussian probability density function as the kernel, a generalized correntropy has been proposed. A generalized maximum correntropy criterion (GMCC) algorithm is then developed by maximizing the generalized correntropy. However, the GMCC algorithm has a high steady-state misalignment and involves a high calculation cost of the exponential term (generalized Gaussian kernel). In this brief, we propose a maximum Versoria criterion (MVC) algorithm, which is derived by maximizing the generalized Versoria function, to reduce steady-state misalignment and computational effort as compared to the GMCC algorithm. The MVC algorithm is then tested in system identification and acoustic echo cancellation scenarios, which have demonstrated that the proposed algorithm is robust against non-Gaussian impulsive noises and performs much better than the LMP and GMCC algorithms.