An online decoupling-whitening frequency domain filtered-error least mean square algorithm for active road noise...

Area of Science:

• Acoustics
• Signal Processing
• Automotive Engineering

Background:

• Active road noise control (ARNC) systems aim to reduce low-frequency noise in vehicle cabins.
• Existing systems often require multiple sensors, leading to slower convergence and higher computational load.
• Challenges include secondary path coupling and reference signal correlation in adaptive algorithms.

Purpose of the Study:

• To propose a novel algorithm for enhanced active road noise control.
• To address the limitations of existing ARNC algorithms, specifically convergence speed and computational complexity.
• To improve the practical implementation of ARNC systems in vehicles.

Main Methods:

• An online decoupling-whitening frequency domain filtered-error least mean square (ODW-FDFeLMS) algorithm is introduced.
• Secondary path decoupling is achieved via inner-outer product decomposition.
• Online reference whitening is performed using spectral factorization.
• Frequency domain processing and filtered-error methods reduce computational complexity.

Main Results:

• Simulations demonstrate superior convergence speed and reduced computational complexity compared to existing algorithms.
• Real-time experiments in a vehicle cabin validate the effectiveness of the ODW-FDFeLMS algorithm.
• The proposed method significantly enhances ARNC performance.

Conclusions:

• The ODW-FDFeLMS algorithm effectively overcomes the limitations of traditional ARNC methods.
• The algorithm provides a practical and efficient solution for real-time road noise control in vehicles.
• This advancement contributes to improved vehicle cabin comfort through superior noise attenuation.