最大相关熵准则自适应滤波器的分数阶长算法

对自适应滤波器最佳阶长的准确估计可以有效平衡自适应算法的稳态性能与计算复杂度,而基于最小均方差(MMSE)准则的变阶长最小均方(LMS)算法在非高斯噪声环境下的收敛性能变差。针对这一问题,提出一种最大相关熵准则(MCC)自适应滤波器的分数阶长(FT)算法--FT-MCC算法。该算法从MCC自适应滤波器最佳阶长的定义出发,利用不同阶长滤波器产生的相关熵之差实现阶长更新。理论分析和实验表明:相比现有变阶长最小均方算法,FT-MCC算法在非高斯噪声环境中具有较强的鲁棒性;通过恰当的参数选择,算法可较好地实现对最佳阶长的跟踪和估计。

Fractional tap-length algorithm for adaptive filters under maximum correntropy criterion

Accurate estimation of the optimum tap-length for the adaptive filter provides a good balance between the steady-state performance and the complexity of the adaptive algorithm. The convergence performance of the variable tap-length least mean square (LMS) adaptive filters under the minimum mean square error (MMSE) criterion deteriorates in the non-Gaussian noise environment. A fractional tap-length (FT) algorithm for the maximum correntropy criterion (MCC) adaptive filters, named FT-MCC algorithm, is proposed to solve the above problem. The proposed algorithm is based on the concept of the optimum tap-length for the MCC adaptive filters. The difference of the correntropy between adaptive filters with different tap-lengths is used to achieve the tap-length update. Both theoretical analysis and simulation result show that the proposed algorithm has strong robustness in non-Gaussian noise environment compared with other variable tap-length LMS algorithm and the optimum tap-length can be well estimated with proper parameter selection.