An online secondary path modeling method with regularized step size and self-tuning power scheduling

Area of Science:

• Acoustics
• Signal Processing
• Control Systems

Background:

• Active noise control (ANC) systems aim to reduce unwanted sound.
• Accurate modeling of the secondary path is crucial for ANC performance.
• Existing methods may struggle with convergence speed and accuracy, especially under perturbations.

Purpose of the Study:

• To improve active noise control algorithms through enhanced online secondary path modeling.
• To develop a more stable and accurate ANC system that converges faster.
• To reduce target noise effectively even in the presence of strong perturbations.

Main Methods:

• The proposed method utilizes three adaptive filters for system convergence tracking and noise reduction.
• Theoretical analysis derives an optimized step size and injected random noise gain.
• Step size adapts to filter convergence, while noise gain relates to modeling error power for stability.

Main Results:

• The method demonstrates improved convergence rate and estimation accuracy for both the ANC system and secondary path modeling.
• Enhanced stability is achieved, even with significant perturbations.
• Computational complexity increase is minimized compared to previous approaches.

Conclusions:

• The novel approach offers superior performance in active noise control and secondary path modeling.
• The adaptive step size and error-proportional noise gain contribute to robustness and efficiency.
• Simulation results confirm the effectiveness across various noise types.