In this study, three deconvolution approaches increasing the accuracy of conventional beamforming were reviewed by numerical simulation. One of the approaches is “Deconvolution approach for the mapping of acoustic sources” (DAMAS). It regarded conventional beamforming as a system of linear equations and solved it by Gauss-SeidelMethod (GSM). The other method is “Sparsity constrained deconvolution approaches for acoustic mapping” (SCDAMAS). It assumed the elements of linear equations to be sparsity constrained. Which made it a quadratic convex optimization problem. Solving it by “least absolute shrinkage and selection operator” (LASSO) algorithm. Finally, there is “Covariance matrix fitting” (CMF). It established an optimization problem by sampled cross spectral matrix (CSM). All of them eliminated the energy spread by the sound source location and performed better when frequency increased.