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High-resolution source localization exploiting the sparsity of the beamforming map

Ding, Xinghao ; Liang, Hao ; Jakobsson, Andreas LU orcid ; Tu, Xiaotong LU orcid and Huang, Yue (2022) In Signal Processing 192.
Abstract

Beamforming technology plays a significant role in source localization and quantification. As traditional delay-and-sum beamformers generally yield low spatial resolution, as well as suffer from the occurrence of spurious sources, different forms of deconvolution methods have been proposed in the literature. In this work, we propose two approaches based on a sparse reconstruction framework combined with the use of the Fourier-based efficient implementation techniques. Numerical simulations and experimental data analysis show the effectiveness and advantages of the proposed methods.

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author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Acoustic imaging, Array signal processing, Beamforming, Source localization, Sparse representation
in
Signal Processing
volume
192
article number
108377
publisher
Elsevier
external identifiers
  • scopus:85119044638
ISSN
0165-1684
DOI
10.1016/j.sigpro.2021.108377
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2021 Elsevier B.V.
id
fbdde625-69c1-4f1e-803b-d42ee59816ae
date added to LUP
2021-12-02 12:27:26
date last changed
2022-04-27 06:16:45
@article{fbdde625-69c1-4f1e-803b-d42ee59816ae,
  abstract     = {{<p>Beamforming technology plays a significant role in source localization and quantification. As traditional delay-and-sum beamformers generally yield low spatial resolution, as well as suffer from the occurrence of spurious sources, different forms of deconvolution methods have been proposed in the literature. In this work, we propose two approaches based on a sparse reconstruction framework combined with the use of the Fourier-based efficient implementation techniques. Numerical simulations and experimental data analysis show the effectiveness and advantages of the proposed methods.</p>}},
  author       = {{Ding, Xinghao and Liang, Hao and Jakobsson, Andreas and Tu, Xiaotong and Huang, Yue}},
  issn         = {{0165-1684}},
  keywords     = {{Acoustic imaging; Array signal processing; Beamforming; Source localization; Sparse representation}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{Signal Processing}},
  title        = {{High-resolution source localization exploiting the sparsity of the beamforming map}},
  url          = {{http://dx.doi.org/10.1016/j.sigpro.2021.108377}},
  doi          = {{10.1016/j.sigpro.2021.108377}},
  volume       = {{192}},
  year         = {{2022}},
}