Abstract: Dipole beamforming methods exhibit weak correlation between imaging results and equipment operational mechanisms in rotating blade noise localization, whereas characteristic frequencies directly map equipment operational mechanisms to acoustic signals. This study proposes an optimization algorithm using characteristic frequencies as imaging frequencies, conducting systematic simulations and experiments. Using axial fan blades as the primary subject, it compares the imaging performance and computational efficiency of the proposed method with those of monopole mode combination beamforming, and further applies it to locating aerodynamic noise from unmanned aerial vehicle (UAV) propellers. Results demonstrate that this method can identify noise directivity while reducing computation time to one-quarter of that required by the monopole method. By analyzing the angle between the imaging main lobe and the blade's radial or trailing edge direction, combined with directivity analysis, it is determined that axial fan blades generate separation flow noise, while drone propellers primarily produce trailing edge noise and load noise. The results indicate that the dipole beam-based rapid blade noise localization method exhibits strong engineering applicability. These findings provide technical support for industrial machinery noise control and acoustic optimization of low-altitude aircraft.