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The Data-Reusing MCC-Based Algorithm and Its Performance Analysis

机译：基于MCC的数据重用算法及其性能分析

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摘要

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.
机译：最大熵准则（MCC）为非高斯信号处理提供了鲁棒的最优准则。本文通过重用过去的K个输入向量，对传统的基于MCC的自适应滤波算法的权重更新方程进行了修改，形成了一类基于数据重用的基于MCC的算法，称为DRMCC算法。与传统的基于MCC的算法相比，当输入数据相关时，DR-MCC算法提供了更好的收敛性能。理论上研究了DRMCC算法的均方稳定性边界。对于高斯噪声情况和非高斯噪声情况，都导出了DR-MCC算法的稳态超均方误差（EMSE）的表达式。还分析了数据重用顺序与稳态EMSE之间的关系。仿真结果与理论分析吻合。

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