The Random Data-Reusing GMCC Adaptive Filtering Algorithm for System Identification Under Impulsive Noise Environments

The generalized maximum correntropy criterion (GMCC) has been widely applied for robust adaptive filtering (AF) algorithm. The gradient-based GMCC (GB-GMCC) algorithm realizes good filtering performance for system identification under impulsive noise environments. However, the highly colored input signal can damage the convergence rate of GB-GMCC. Therefore, based on the data-reusing method, we propose a robust AF algorithm, called as data-reusing GMCC (DR-GMCC) algorithm, which uses the information of the latest $K$ input data to remedy the convergence limitation of GB-GMCC. In addition, to enhance the filtering performance of DR-GMCC, we use a random strategy to select the past $K$ input data leading to a new algorithm, named as random DR-GMCC (RDR-GMCC). Furthermore, for RDR-GMCC, we also analyze the mean-square convergence and computational complexity. Compared with existing algorithms, simulation results verify that RDR-GMCC achieves better filtering accuracy and faster convergence rate.