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

Zhigang Chu | Zendy; Caihui Chen | Zendy; Yang Yang | Zendy; Linbang Shen | Zendy

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

Author(s) -

Zhigang Chu,

Caihui Chen,

Yang Yang,

Linbang Shen

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2863052

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

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.

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