The data-reusing MCC-based algorithm and its performance analysis

Maximum correntropy criterion (MCC) provides a robust optimality criterion for non-Gaussian signal processing. In this paper, the weight update equation of the conventional MCC-based adaptive filtering algorithm is modified by reusing the past K input vectors, forming a class of data-reusing MCC-based algorithm, called DRMCC algorithm. Comparing with the conventional MCCbased algorithm, the DR-MCC algorithm provides a much better convergence performance when the input data is correlated. The mean-square stability bound of the DRMCC algorithm has been studied theoretically. For both Gaussian noise case and non-Gaussian noise case, the expressions for the steady-state Excess mean square error (EMSE) of DR-MCC algorithm have been derived. The relationship between the data-reusing order and the steadystate EMSEs is also analyzed. Simulation results are in agreement with the theoretical analysis.