论文标题
强劲的总计平均M层归一流的子带滤波器自适应算法，用于脉冲噪声和嘈杂输入
Robust Total Least Mean M-Estimate normalized subband filter Adaptive Algorithm for impulse noises and noisy inputs
论文作者
论文摘要
当输入信号与输入信号相关，并且输入和输出信号被高斯噪声污染时，总正方形标准化子带自适应过滤器（TLS-NSAF）算法显示出良好的性能。但是，当脉冲噪声打扰时，TLS-NSAF算法显示了迅速恶化的收敛性能。为了解决此问题，本文提出了强大的总最小值M估计器标准化子带滤波器（TLMM-NSAF）算法。此外，本文还对TLMM-NSAF算法进行了详细的理论性能分析，并获得了算法的稳定步长范围和理论稳态平方平方偏差（MSD）。为了进一步提高算法的性能，我们还提出了算法的新变量步长（VSS）方法。最后，我们提出的算法的鲁棒性以及理论和模拟值的一致性通过在不同噪声模型下的系统识别和回声取消的计算机模拟来验证。
When the input signal is correlated input signals, and the input and output signal is contaminated by Gaussian noise, the total least squares normalized subband adaptive filter (TLS-NSAF) algorithm shows good performance. However, when it is disturbed by impulse noise, the TLS-NSAF algorithm shows the rapidly deteriorating convergence performance. To solve this problem, this paper proposed the robust total minimum mean M-estimator normalized subband filter (TLMM-NSAF) algorithm. In addition, this paper also conducts a detailed theoretical performance analysis of the TLMM-NSAF algorithm and obtains the stable step size range and theoretical steady-state mean squared deviation (MSD) of the algorithm. To further improve the performance of the algorithm, we also propose a new variable step size (VSS) method of the algorithm. Finally, the robustness of our proposed algorithm and the consistency of theoretical and simulated values are verified by computer simulations of system identification and echo cancellation under different noise models.
