基于m估计的组合步长比例归一化自适应子带滤波器

Jianhong Ye, Yi Yu
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引用次数: 0

摘要

基于m估计的比例归一化子带自适应滤波器(M-PNSAF)存在步长不变导致的性能折衷。为了有效地解决这一问题,我们提出通过混合因子的自适应来利用大大小小的步长来提高M-PNSAF的性能。与通过s型函数自适应调整混合因子不同,该算法在每次迭代时使后向子带误差的平方最小,从而找到最优的时变混合因子。最后,在脉冲噪声环境下的仿真结果验证了该算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
M-estimate based Proportionate Normalized Adaptive Subband Filter with Combined Step-size
The M-estimate based proportionate normalized subband adaptive filter (M-PNSAF) exists a performance compromise caused by the constant step-size. To effectively deal with this problem, we propose to take advantage of large and small step-sizes through the adaptation of the mixing factor to improve the M-PNSAF performance. Different from adaptively adjusting the mixing factor through a sigmoid function, the proposed algorithm minimizes the squared a posterior subband errors at each iteration to find the optimal time-varying mixing factor. Finally, simulation results in impulsive noises corroborate the effectiveness of the proposed algorithm.
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