Super-resolution acoustic imaging using sparse recovery with spatial priming

摘要: In this paper, we propose a new strategy to obtain superresolution maps of the sound field recorded by spherical microphone array. recent works, have demonstrated that sparse recovery (SR) algorithms based on minimisation l p norm with 0 when p<1 is it non-convex optimisation problem, thus likely algorithm converges local minimum. paper show can improve convergence our SR acoustic imaging methods providing, solver, priming information relating spatial location sources. This be acquired pre-processing, coarse analysis using standard blind source separation or direction-of-arrival techniques. Simulation results indicate approach provide accurate estimates positions multiple, simultaneous sources in presence noise reverberation and even an under-determined situation.