论文标题
一般强大的子带自适应过滤的研究
Study of General Robust Subband Adaptive Filtering
论文作者
论文摘要
在本文中，我们提出了一种一般稳健的子带自适应滤波（GR-SAF）方案，以防止脉冲噪声，以最大程度地降低具有个体重量不确定性的随机步行模型下的均方偏差。具体而言，通过在GR-SAF方案中选择不同的缩放因子，例如从M-估计和最大correntropy robust标准中，我们可以轻松获得不同的GR-SAF算法。重要的是，所提出的GR-SAF算法可以简化为可变的正则化鲁棒归一化的SAF算法，从而具有快速的收敛速率和低稳态误差。在系统识别的背景下，用冲动噪声和回声取消的模拟验证了所提出的GR-SAF算法的表现优于其对应物。
In this paper, we propose a general robust subband adaptive filtering (GR-SAF) scheme against impulsive noise by minimizing the mean square deviation under the random-walk model with individual weight uncertainty. Specifically, by choosing different scaling factors such as from the M-estimate and maximum correntropy robust criteria in the GR-SAF scheme, we can easily obtain different GR-SAF algorithms. Importantly, the proposed GR-SAF algorithm can be reduced to a variable regularization robust normalized SAF algorithm, thus having fast convergence rate and low steady-state error. Simulations in the contexts of system identification with impulsive noise and echo cancellation with double-talk have verified that the proposed GR-SAF algorithms outperforms its counterparts.
