Super-resolution compressive spherical beamforming based on off-grid sparse Bayesian inference

Compressive spherical beamforming (CSB) with spherical microphone arrays is a panoramic acoustic source identification technology with high spatial resolution and clear acoustic imaging, which has broad application prospects. Due to the discretization of the focus region and the assumption of on-grid sources, classical CSB suffers from the basis mismatch problem, i.e., it faces performance deterioration when identifying off-grid sources. To overcome the problem, this paper proposes off-grid sparse Bayesian inference-based CSB (OGSBI-CSB). OGSBI-CSB formulates the direction of arrival (DOA) as the sum of the DOA of grid point and DOA offset, constructs an off-grid model based on Taylor expansion, and adapts OGSBI to solve the model and obtains DOA and strength estimation. Simulations and experiments demonstrate that the proposed OGSBI-CSB not only can effectively alleviate the basis mismatch problem and then improve identification accuracy for off-grid sources, but also enjoys super-resolution and good resistance to noise interference.

Automated summary

This paper proposes an off-grid sparse Bayesian inference-based compressive spherical beamforming (OGSBI-CSB) method, which effectively solves the basis mismatch problem in traditional CSB and significantly improves identification accuracy, super-resolution, and resistance to noise interference for off-grid sources.