Yue Pengcheng, Qu Hua, Zhao Jihong, Wang Meng
School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Entropy (Basel). 2020 Aug 22;22(9):922. doi: 10.3390/e22090922.
This paper provides a novel Newtonian-type optimization method for robust adaptive filtering inspired by information theory learning. With the traditional minimum mean square error (MMSE) criterion replaced by criteria like the maximum correntropy criterion (MCC) or generalized maximum correntropy criterion (GMCC), adaptive filters assign less emphasis on the outlier data, thus become more robust against impulsive noises. The optimization methods adopted in current MCC-based LMS-type and RLS-type adaptive filters are gradient descent method and fixed point iteration, respectively. However, in this paper, a Newtonian-type method is introduced as a novel method for enhancing the existing body of knowledge of MCC-based adaptive filtering and providing a fast convergence rate. Theoretical analysis of the steady-state performance of the algorithm is carried out and verified by simulations. The experimental results show that, compared to the conventional MCC adaptive filter, the MCC-based Newtonian-type method converges faster and still maintains a good steady-state performance under impulsive noise. The practicability of the algorithm is also verified in the experiment of acoustic echo cancellation.
本文提出了一种受信息理论学习启发的用于鲁棒自适应滤波的新型牛顿型优化方法。通过用诸如最大相关熵准则（MCC）或广义最大相关熵准则（GMCC）等准则取代传统的最小均方误差（MMSE）准则，自适应滤波器对异常数据的重视程度降低，从而对脉冲噪声具有更强的鲁棒性。当前基于MCC的LMS型和RLS型自适应滤波器所采用的优化方法分别是梯度下降法和定点迭代法。然而，本文引入了一种牛顿型方法，作为一种新颖的方法来增强基于MCC的自适应滤波的现有知识体系，并提供快速收敛速度。对该算法的稳态性能进行了理论分析，并通过仿真进行了验证。实验结果表明，与传统的MCC自适应滤波器相比，基于MCC的牛顿型方法收敛更快，并且在脉冲噪声下仍保持良好的稳态性能。该算法的实用性也在声回波抵消实验中得到了验证。
