Maximum Total Complex Correntropy for Adaptive Filter

Abstract

Nowadays, complex Correntropy has been widely used for adaptive filtering in the complex domain. Compared with the second order statistics methods, the complex correntropy based algorithms have shown the superiority in the non-Gaussian noise, especially the impulsive noise. However, the current complex correntropy based adaptive filtering algorithms have not taken the input noise into consideration, and the performances will be deteriorated when the input signals are also corrupted by the noise. In this article, we focus on the errors-in-variables (EIV) model and propose an adaptive algorithm based on the maximum total complex correntropy (MTCC). More importantly, we present the local stability analysis and derive the theoretical weight error power. Simulation results confirm the validity of the theoretical analysis and illustrate the superior performance of the propose algorithm in the EIV cases.