Improving the efficiency of DAMAS for sound source localization via wavelet compression computational grid

Abstract

Phased microphone arrays are used widely in the applications for acoustic source localization. Deconvolution approaches such as DAMAS successfully overcome the spatial resolution limit of the conventional delay-and-sum (DAS) beamforming method. However deconvolution approaches require high computational effort compared to conventional DAS beamforming method. This paper presents a novel method that serves to improve the efficiency of DAMAS via wavelet compression computational grid rather than via optimizing DAMAS algorithm. In this method, the efficiency of DAMAS increases with compression ratio. This method can thus save lots of run time in industrial applications for sound source localization, particularly when sound sources are just located in a small extent compared with scanning plane and a band of angular frequency needs to be calculated. In addition, this method largely retains the spatial resolution of DAMAS on original computational grid, although with a minor deficiency that the occurrence probability of aliasing increasing slightly for complicated sound source.

Robust Acoustic Imaging Based on Bregman Iteration and Fast Iterative Shrinkage-Thresholding Algorithm

“…The coefficients d j l of interpolating wavelets could be fast calculated by hierarchical applying interpolating on level j − 1 to the difference set between j resolution and j − 1 resolution. As previously described [42][43][44], d j l is derived by subtracting interpolating value from the true value in level J. The optimized computational grids could be formed via extracting essential points with the wavelets method.…”

Theoretical analysis of the DAMAS algorithm and efficient implementation of the covariance matrix fitting method for large-scale problems

“…Several methods, most of them numerically more efficient than the DAMAS algorithm, have been proposed since 2006 (see [2] for a review of such methods), and are frequently compared with it, one recurring remark being the computational effort that the DAMAS algorithm requires [3][4][5][6][7][8][9][10][11][12] . Among these methods, several variants of the DAMAS algorithm with improved numerical efficiency have been proposed, based on the formulation of the problem in the Fourier domain [13], or pruning of the grid points that will not be involved in the final source map either directly [10,11], or similarly in a wavelet basis [8]. In the former case, formulation in the Fourier domain is limited to specific geometries, while in the latter, even after pruning, the computational complexity remains too high for large scale problems.…”

A Robust and Efficient Compressed Sensing Algorithm for Wideband Acoustic Imaging

“…Acoustic imaging, which uses planar microphone array and beamforming methods [1][2][3][4][5], is widely employed for locating acoustic sources. As for wideband acoustic imaging, one way is to operate the time-domain beamforming technique.…”