High-resolution source localization exploiting the sparsity of the beamforming map
(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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/fbdde625-69c1-4f1e-803b-d42ee59816ae
- author
- Ding, Xinghao ; Liang, Hao ; Jakobsson, Andreas LU ; Tu, Xiaotong LU and Huang, Yue
- organization
- publishing date
- 2022-03
- 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}}, }