DAMAS with compression computational grid based on functional beamforming for acoustic source localization

In recent years, the awareness of the impact of noise on health has increased significantly, and consequently sound source localization has been increasingly used in noise diagnostics. Phased microphone arrays have become a standard technique for acoustic source localization. Compared with beamforming algorithms such as the conventional beamforming as well as the function beamforming, deconvolution approaches such as DAMAS successfully improve the spatial resolution. However deconvolution approaches usually require high computational effort compared with beamforming methods. Without optimizing deconvolution algorithm, recently DAMAS with compression computational grid beased on the conventional beamforming (DAMAS-CG2) has reduced significantly computational run time of DAMAS in applications (Ma & Liu, J. Sound Vib., 2017). This paper proposes a novel algorithm that DAMAS with a novel compression computational grid based on the functional beamforming (denoted by DAMAS-CG3). Application simulations and an airfoil trailing edge noise experiment show that DAMAS- CG3 can obtain larger compression ratio compared with DAMAS-CG2, and thus is more efficient than DAMAS-CG2.