Two-Dimensional Total Variation Norm Constrained Deconvolution Beamforming Algorithm for Acoustic Source Identification

Acoustic source identification algorithms based on planar microphone array are widely used. In this paper, a novel total variation (TV) norm constrained deconvolution algorithm for acoustic source identification is proposed. The paper builds the model of deconvolution convex optimization problem, derives two deviation operation matrices and solves 2-D TV norm constrained deconvolution problem. Identification imaging and standard deviations of different algorithms are compared in the simulations. The results indicate that the proposed algorithm not only has good point source recognition performance, but also identifies extended sources accurately, and the standard deviation is minimum.