Wavespace-Based Coherent Deconvolution

摘要: Array deconvolution is commonly used in aeroacoustic analysis to remove the influence of a microphone array's point spread function from conventional beamforming map. Unfortunately, majority algorithms assume that acoustic sources measurement are incoherent, which can be problematic for some phenomena with coherent, spatially-distributed characteristics. While several have been proposed handle coherent sources, computationally intractable many problems while others require restrictive assumptions about source field. Newer generalized inverse techniques hold promise, but still under investigation general use. An alternate method based on wavespace transformation array data. Wavespace offers advantages over curved-wave processing, such as providing an explicit shift-invariance convolution sampling wave However, usage assumes field accurately approximated superposition plane fields, regardless true wavefront curvature. The technique leverages Fourier transforms quickly evaluate shift-invariant convolution. derived and applied ideal incoherent fields demonstrate its ability determine magnitude relative phase multiple sources. Multi-scale processing explored means accelerating solution convergence. A case spherical front evaluated. Finally, trailing edge noise experiment considered. Results show successfully deconvolves partially-coherent, degree necessary quantitative evaluation. Curved cases warrant further investigation. potential extension nearfield proposed.