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Robust normalized subband adaptive filter algorithm against impulsive noises and noisy inputs

机译：鲁棒归一化子带自适应滤波算法抗脉冲噪声和嘈杂的输入

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摘要

This paper proposes a robust normalized subband adaptive filter (RNSAF) algorithm, which has robust performance for impulsive noise environments and noisy inputs. Although the M-estimate normalized subband adaptive filter (MNSAF) algorithm achieves robustness against impulsive noises, it generates biased estimates when the input is noisy. Based on the unbiasedness criterion, we propose a bias-compensation vector added in the RNSAF algorithm to compensate for the bias resulting from input noises. The statistical analysis reveals that the RNSAF algorithm can provide unbiased estimates. The stability analysis is also performed and the stability conditions are obtained. Moreover, by minimization of the mean-square deviation, a variable step size scheme is derived to achieve better performance. Sim-ulation results in the context of system identification demonstrate that the proposed algorithm not only obtains robust performance in the impulsive noise environment but also achieves improved performance under noisy inputs. (c) 2020 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
机译：本文提出了一种强大的归一化子带自适应滤波器（RNSAF）算法，其对脉冲噪声环境和噪声输入具有鲁棒性能。尽管M估计归一化子带自适应滤波器（MNSAF）算法实现了对脉冲噪声的鲁棒性，但是当输入噪声时，它会产生偏置估计。基于非偏见标准，我们提出了一种在RNSAF算法中添加的偏置补偿矢量，以补偿由输入噪声产生的偏置。统计分析表明，RNSAF算法可以提供无偏估计。还进行了稳定性分析，获得稳定性条件。此外，通过最小化平均方偏差，导出可变步长方案以实现更好的性能。在系统识别的背景下，SIM-ULATION结果证明了所提出的算法不仅在脉冲噪声环境中获得鲁棒性能，而且还达到了噪声输入下的提高性能。 （c）2020富兰克林学院。 elsevier有限公司出版。保留所有权利。

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  • 来源
    《Journal of the Franklin Institute》 |2020年第5期|3113-3134|共22页
  • 作者

    Zheng Zongsheng; Liu Zhigang; Lu Xiaobing;

  • 作者单位

    Southwest Jiaotong Univ Sch Elect Engn Chengdu Peoples R China;

    Southwest Jiaotong Univ Sch Elect Engn Chengdu Peoples R China;

    Southwest Jiaotong Univ Sch Elect Engn Chengdu Peoples R China;

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