Statistics variable kernel width for maximum correntropy criterion algorithm

摘要

Since the maximum correntropy criterion (MCC) algorithm with a constant kernel width leads to the trade-off problem between the convergence rate and steady-state misalignment, various adaptive kernel width MCC algorithms were derived to solve this problem. However, the superior performances of these algorithms depend mainly on specific data range, or have complicated calculation and parameter setting. Thus, this paper proposes a statistics variable kernel width MCC (SVKW-MCC) algorithm to overcome these problems. Specifically, the proposed algorithm calculates the mean and variances of the errors signal, and then the proposed algorithm removes these data that significantly deviate from the mean value of errors signal, moreover, the new mean and variance are recalculated after removing these abnormal data, subsequently, the new kernel width is calculated by the new variance and mean. Simulation results in system identification and echo cancellation scenarios show that the proposed algorithm outperforms the existing variable kernel width methods. Moreover, the stability and steady-state mean-square performance of the proposed algorithm is analyzed and verified by experiments. More importantly, the new method involves no extra free parameters and does not depend on the specific application data range, so the proposed algorithm has a very good application prospect.