A fast deconvolution method for multiple sound source localization based on Hilbert curve

Abstract

To improve the resolution and efficiency of the multiple sound source localization, a fast deconvolution method based on the Hilbert curve, called CLEAN-VH, is developed in this paper. Firstly, the initial cross-spectral matrix of the measured array signal is decomposed by eigenvalue decomposition, and a new cross-spectral matrix function is constructed using the eigenvalues and eigenvectors. Then, the function is used to update the cross-spectral matrix in the process of deconvolution iteration (CLEAN), and the Hilbert curve is introduced to improve the computational efficiency of the output power in the monitoring area. Finally, the distribution of multiple sound sources can be obtained by superimposing the clean beams and residual power spectrum. The superiority of CLEAN-VH is proved by analyzing the key parameters of the method. The search area optimization based on Hilbert curve greatly alleviates the grid mismatch problem and improves the performance of the proposed method. Through the simulation and experimental comparisons with the other three algorithms, such as CLEAN, FB and CLEAN-SC, the proposed CLEAN-VH achieves high-resolution localization of multiple sound sources, better dynamic range and higher computation efficiency. The proposed method provides another choice for the localization of multiple sound sources.