Abstract: A fast deconvolution approach for the mapping of coherent acoustic sources (FC-DAMAS) is proposed in the paper. The approach eliminates the cross spectrum process in the DAMAS and directly solves the complex strength of the sound source, which avoids the increase of the number of unknowns caused by the cross-spectrum operation. Therefore, it is no longer necessary to use the non-coherent source hypothesis to reduce the number of unknowns, so that the algorithm can be applied to both coherent and non-coherent sources Secondly, the approach uses the L1 norm sparse constrained deconvolution method similar to the sparsity constrained DAMAS approach(SC-DAMAS), which makes the approach has high computational precision and spatial resolution in the identification process of coherent and non-coherent sound sources. In addition, a principal component analysis de-noising process applied to the measured sound pressure is added in the proposed approach, which compensates the aberration of the algorithm caused by canceling the cross-spectrum de-noising process and so that the approach has similarity to the noise robustness of the SC-DAMAS. Furthermore, compared with the deconvolution approach for the coherent sources (DAMAS-C), the proposed FC-DAMAS greatly reduces the scale of the matrix equation to be solved, so that its computational efficiency is improved significantly. In this paper, the advantages of FC-DAMAS are verified by numerical simulation and experiment. The results show that the proposed FC-DAMAS has advantages in terms of application range, sound source recognition performance and practicability, and is more suitable for practical application.

Key words: beamforming, coherent sound sources, deconvolution, sound source identification