Orthogonal matching pursuit applied to the deconvolution approach for the mapping of acoustic sources inverse problem.

Microphone arrays and beamforming have become a standard method to localize aeroacoustic sources. Deconvolution techniques have been developed to improve spatial resolution of beamforming maps. The deconvolution approach for the mapping of acoustic sources (DAMAS) is a standard deconvolution technique, which has been enhanced via a sparsity approach called sparsity constrained deconvolution approach for the mapping of acoustic sources (SC-DAMAS). In this paper, the DAMAS inverse problem is solved using the orthogonal matching pursuit (OMP) and compared with beamforming and SC-DAMAS. The resulting noise source maps show that OMP-DAMAS is an efficient source localization technique in the case of uncorrelated or correlated acoustic sources. Moreover, the computation time is clearly reduced as compared to SC-DAMAS.