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<p>The augmented complex-valued normalized subband adaptive filtering algorithm exhibits excellent convergence performance in the processing of highly correlated non-circular complex-valued signals, but it lacks robustness against impulsive noise. Although the recently developed augmented complex-valued normalized M-estimate subband adaptive filtering (ACNMSAF) algorithm improves robustness via the complex-valued modified Huber function, its adaptive threshold and MSE equivalence under non-impulsive noise conditions cause significant performance degradation in non-Gaussian or frequent impulsive noise environments. To address this problem, this paper proposes an augmented complex-valued normalized logarithmic subband adaptive filtering (ACNLSAF) algorithm based on the relative logarithmic function. Furthermore, by equating the subband a posteriori error variance to the corresponding subband background noise variance, a subband-level variable step-size ACNLSAF (VSS-ACNLSAF) algorithm is designed, which achieves a significant improvement in convergence performance at the cost of higher computational complexity. Subsequently, the stability and steady-state excess mean square error (EMSE) of the ACNLSAF algorithm are analyzed. Extensive simulations in system identification and stereo acoustic echo cancellation (SAEC) validate the accuracy of the theoretical EMSE model and demonstrate the superiority of the proposed algorithms over the existing ACNMSAF algorithm in terms of estimation accuracy, particularly in non-Gaussian and frequent impulsive noise environments.</p>

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A Robust Logarithmic Cost-Based Complex-Valued Subband Filtering Algorithm Against Non-Gaussian Noise Interference

  • 期刊论文

  • 发表日期：

    2026年7月16日

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  • Shaohui Lv,
  • Haiquan Zhao,
  • Wenjing Xu

摘要

The augmented complex-valued normalized subband adaptive filtering algorithm exhibits excellent convergence performance in the processing of highly correlated non-circular complex-valued signals, but it lacks robustness against impulsive noise. Although the recently developed augmented complex-valued normalized M-estimate subband adaptive filtering (ACNMSAF) algorithm improves robustness via the complex-valued modified Huber function, its adaptive threshold and MSE equivalence under non-impulsive noise conditions cause significant performance degradation in non-Gaussian or frequent impulsive noise environments. To address this problem, this paper proposes an augmented complex-valued normalized logarithmic subband adaptive filtering (ACNLSAF) algorithm based on the relative logarithmic function. Furthermore, by equating the subband a posteriori error variance to the corresponding subband background noise variance, a subband-level variable step-size ACNLSAF (VSS-ACNLSAF) algorithm is designed, which achieves a significant improvement in convergence performance at the cost of higher computational complexity. Subsequently, the stability and steady-state excess mean square error (EMSE) of the ACNLSAF algorithm are analyzed. Extensive simulations in system identification and stereo acoustic echo cancellation (SAEC) validate the accuracy of the theoretical EMSE model and demonstrate the superiority of the proposed algorithms over the existing ACNMSAF algorithm in terms of estimation accuracy, particularly in non-Gaussian and frequent impulsive noise environments.