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<p>This paper presents an advanced hybrid adaptive filtering algorithm that integrates the Filtered-X Least Mean Squares (FXLMS) method and the Reweighted Sparse Least Mean Mixed-Norm (LMMN) approach to enhance the performance of active noise control (ANC) systems. The proposed algorithm incorporates the robustness of FXLMS to account for secondary path effects, speed and accuracy of LMMN for fast convergence and minimal steady-state error, and a trigonometric FLANN expansion to effectively handle nonlinearities in acoustic environments. The inclusion of the FLANN expansion extends the algorithm’s ability to model complex, nonlinear noise patterns, making it suitable for dynamic and challenging environments. Furthermore, the reweighted sparsity penalty in LMMN ensures efficient adaptation by promoting a sparse filter structure. Simulation results demonstrate that the proposed FFRMN algorithm achieves up to 5dB lower initial steady-state NMSE for real sparse paths in feedforward nonlinear ANC and approximately 15 dB improvement in feedback nonlinear ANC compared to VFXLMS and FSLMS algorithms, while maintaining faster convergence.</p>

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A Trigonometric FLANN-Enhanced Filtered-X LMS and Reweighted Sparse LMMN Algorithm for Nonlinear Active Noise Control

  • 期刊论文

  • 发表日期：

    2026年5月29日

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  • Rosalin,
  • Ansuman Patnaik

摘要

This paper presents an advanced hybrid adaptive filtering algorithm that integrates the Filtered-X Least Mean Squares (FXLMS) method and the Reweighted Sparse Least Mean Mixed-Norm (LMMN) approach to enhance the performance of active noise control (ANC) systems. The proposed algorithm incorporates the robustness of FXLMS to account for secondary path effects, speed and accuracy of LMMN for fast convergence and minimal steady-state error, and a trigonometric FLANN expansion to effectively handle nonlinearities in acoustic environments. The inclusion of the FLANN expansion extends the algorithm’s ability to model complex, nonlinear noise patterns, making it suitable for dynamic and challenging environments. Furthermore, the reweighted sparsity penalty in LMMN ensures efficient adaptation by promoting a sparse filter structure. Simulation results demonstrate that the proposed FFRMN algorithm achieves up to 5dB lower initial steady-state NMSE for real sparse paths in feedforward nonlinear ANC and approximately 15 dB improvement in feedback nonlinear ANC compared to VFXLMS and FSLMS algorithms, while maintaining faster convergence.