Recently, the maximum correntropy criterion (MCC) has been successfully applied in numerous applications regarding nonGaussian data processing. MCC employs a free parameter called kernel width, which affects the convergence rate, robustness, and steady-state performance of the adaptive filtering. However, determining the optimal value for such parameter is not always a trivial task. Within this context, this paper proposes a novel method called adaptive convex combination maximum correntropy criterion (ACCMCC), which combines an adaptive kernel algorithm with convex combination techniques. ACCMCC takes advantage from a convex combination of two adaptive MCC-based filters, whose kernel widths are adjusted iteratively as a function of the minimum error value obtained in a predefined estimation window. Results obtained in impulsive noise environment have shown that the proposed approach achieves equivalent convergence rates but with increased accuracy and robustness when compared with other similar algorithms reported in literature.

中文翻译：

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最大熵准则的自适应核宽凸组合方法

最近，最大熵准则（MCC）已成功地应用于有关非高斯数据处理的众多应用中。MCC使用称为内核宽度的自由参数，该参数会影响自适应滤波的收敛速度，鲁棒性和稳态性能。但是，确定此类参数的最佳值并不总是一件容易的事。在此背景下，本文提出了一种新的方法，称为自适应凸组合最大熵准则（ACCMCC），该方法将自适应核算法与凸组合技术相结合。ACCMCC受益于两个基于MCC的自适应滤波器的凸组合，其内核宽度根据在预定义的估计窗口中获得的最小误差值进行迭代调整。