Convex Combination of Multiple Adaptive Filters under the Maximum Correntropy Criterion
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摘要: 基于最大互相关熵准则(MCC)的自适应滤波算法在非高斯噪声环境下具有强鲁棒性，得到了广泛应用。然而，传统MCC滤波算法在选择参数时依然受到收敛速度与稳态精度之间固有矛盾的困扰。为解决这一问题，该文提出一类多凸组合MCC算法，能够充分发挥不同参数组合下滤波算法的性能优势，从而获得更好的信道跟踪能力。理论分析得出了所提算法的均值收敛条件和稳态均方误差，同时，仿真实验表明所提算法在对抗高斯和非高斯噪声时均具有收敛快、稳态精度高的特点。Abstract: The adaptive filtering algorithms under the Maximum Correntropy Criterion (MCC) show strong robustness against impulsive noises. The original MCC adaptive filter, however, still suffers from a compromise between convergence rate and misadjustment when choosing parameters. To address this issue, a convex combination approach is proposed in this paper, where multiple MCC adaptive filters with different step-sizes and kernel widths are combined together to yield fast convergence speed and lower misadjustment. Theoretical analysis on convergence of the new approach demonstrates that it can achieve more desirable performance than the original MCC adaptive filter as well as convex combination of two MCC adaptive filers with different step-sizes or kernel widths. Simulation results confirm the excellent performance of the new method.
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表 1 算法参数设置
名称 ${\mu _1}$/${\sigma _1}$ ${\mu _2}$/${\sigma _2}$ ${\mu _3}$/${\sigma _3}$ ${\mu _4}$/${\sigma _4}$ ${\mu _\xi }$/${\sigma _\xi }$ 数值 0.03/6.0 0.04/4.0 0.08/2.0 0.3/1.5 4.5/1.0 
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