Combined Step-size Affine Projection Mix-norm Algorithm with Generalized Maximum Correntropy for Robust Filtering

A cluster of affine projection (AP) algorithms has the characteristics of a fast convergence rate under coloured noise. Furthermore, robust mixed-norm algorithms (RMNA) have been confirmed to possess outstanding steady-state properties. Simultaneously, combined step-size methods have the competence to ameliorate the tracking competence of AP-type algorithms. This paper proposes a novel combined step-size affine projection mixed-norm algorithm with a generalized maximum correntropy criterion for robust filtering named CSS-APRMNGMC. The proposed CSS-APRMNGMC integrates the advantages of mix-norm and generalized maximum correntropy criterion and applies new combined step-size methods to ameliorate tracking ability. The superior performance is verified in identification of system, which contains non-gaussian impulsive interferences.