An optimal sensor layout method based on noise reduction estimation for active road noise control

Abstract

Active noise control (ANC) is an effective method to suppress vehicle road noise. The rapid selection of the reference sensor set and rational selection of its layout position are crucial for determining the vibration noise transmission paths with high contributions, which determines the noise reduction performance of the active road noise control (ARNC) system. The reference sensor set selected by the conventional multiple coherence function (MCOH) method does not always ensure the optimal noise reduction performance and the matrix singularity problem based on the Fisher information matrix (FIM) expansion method. To alleviate this problem, this paper proposes a method based on the overall level of noise reduction (OANR) and the FIM to select a set of reference sensors and determine their positions. This method uses an optimal causally constrained Wiener filter to estimate the theoretical OANR for each control area to ensure the reference sensor selected has the optimal noise reduction performance, and it expands efficiently based on the FIM method to select a set of reference sensors. Besides, the matrix singularity problems in the FIM-based expansion method are solved using noise frequency summation and matrix operation, which makes the method more universally applicable. Numerical simulations are performed to analyze and evaluate the noise reduction performance and selection efficiency of the proposed method under different conditions. In addition, a series of real vehicle ANC experiments are conducted. The results show that the reference sensor set selected based on the proposed method has an obvious advantage in noise reduction performance, and only four reference sensors are needed to achieve 4.63 dB(A) noise reduction in ARNC experiments. It fully confirms the effectiveness of the proposed method in real vehicle applications.