Compressive spherical beamforming based on fast off-grid sparse Bayesian inference

Compressive spherical beamforming (CSB) with spherical microphone arrays not only inherits the high spatial resolution and strong sidelobe suppression of compressive beamforming but also achieves panoramic acoustic source identification owing to the rotational symmetry of spherical microphone arrays, which is an interesting topic in the field of acoustic source identification. The recent off-grid sparse Bayesian inference-based CSB (OGSBI-CSB) can effectively overcome the basis mismatch of earlier on-grid CSB approaches and shows a higher resolution than the Newtonized orthogonal matching pursuit-based CSB (NOMP-CSB), however, it is severely time-consuming. Therefore, this paper proposes fast OGSBI-CSB (FOGSBI-CSB), which first solves an on-grid CSB model using sparse Bayesian inference to estimate the initial directions of arrival (DOAs), then performs DOA refinement by discretizing the local regions centered on the initial on-grid DOAs into finer grids and searching for candidates that can maximize the cost function, and finally quantifies source strengths utilizing the least squares method. Simulation and experimental results demonstrate that the proposed FOGSBI-CSB could provide a higher resolution than NOMP-CSB and a higher computational efficiency and resolution than OGSBI-CSB.