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Smart Ocean: A New Fast Deconvolved Beamforming Algorithm for Multibeam Sonar

A new fast deconvolved beamforming algorithm is proposed in this paper, and it can greatly reduce the computation complexity of the original Richardson–Lucy (R–L algorithm) deconvolution algorithm by utilizing the convolution theorem and the fast Fourier transform technique. This algorithm makes it...

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Detalles Bibliográficos
Autores principales: Huang, Jie, Zhou, Tian, Du, Weidong, Shen, Jiajun, Zhang, Wanyuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6263529/
https://www.ncbi.nlm.nih.gov/pubmed/30453637
http://dx.doi.org/10.3390/s18114013
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author Huang, Jie
Zhou, Tian
Du, Weidong
Shen, Jiajun
Zhang, Wanyuan
author_facet Huang, Jie
Zhou, Tian
Du, Weidong
Shen, Jiajun
Zhang, Wanyuan
author_sort Huang, Jie
collection PubMed
description A new fast deconvolved beamforming algorithm is proposed in this paper, and it can greatly reduce the computation complexity of the original Richardson–Lucy (R–L algorithm) deconvolution algorithm by utilizing the convolution theorem and the fast Fourier transform technique. This algorithm makes it possible for real-time high-resolution beamforming in a multibeam sonar system. This paper applies the new fast deconvolved beamforming algorithm to a high-frequency multibeam sonar system to obtain a high bearing resolution and low side lobe. In the sounding mode, it restrains the tunnel effect and makes the topographic survey more accurate. In the 2D acoustic image mode, it can obtain clear images, more details, and can better distinguish two close targets. Detailed implementation methods of the fast deconvolved beamforming are given, its computational complexity is analyzed, and its performance is evaluated with simulated and real data.
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spelling pubmed-62635292018-12-12 Smart Ocean: A New Fast Deconvolved Beamforming Algorithm for Multibeam Sonar Huang, Jie Zhou, Tian Du, Weidong Shen, Jiajun Zhang, Wanyuan Sensors (Basel) Article A new fast deconvolved beamforming algorithm is proposed in this paper, and it can greatly reduce the computation complexity of the original Richardson–Lucy (R–L algorithm) deconvolution algorithm by utilizing the convolution theorem and the fast Fourier transform technique. This algorithm makes it possible for real-time high-resolution beamforming in a multibeam sonar system. This paper applies the new fast deconvolved beamforming algorithm to a high-frequency multibeam sonar system to obtain a high bearing resolution and low side lobe. In the sounding mode, it restrains the tunnel effect and makes the topographic survey more accurate. In the 2D acoustic image mode, it can obtain clear images, more details, and can better distinguish two close targets. Detailed implementation methods of the fast deconvolved beamforming are given, its computational complexity is analyzed, and its performance is evaluated with simulated and real data. MDPI 2018-11-17 /pmc/articles/PMC6263529/ /pubmed/30453637 http://dx.doi.org/10.3390/s18114013 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Huang, Jie
Zhou, Tian
Du, Weidong
Shen, Jiajun
Zhang, Wanyuan
Smart Ocean: A New Fast Deconvolved Beamforming Algorithm for Multibeam Sonar
title Smart Ocean: A New Fast Deconvolved Beamforming Algorithm for Multibeam Sonar
title_full Smart Ocean: A New Fast Deconvolved Beamforming Algorithm for Multibeam Sonar
title_fullStr Smart Ocean: A New Fast Deconvolved Beamforming Algorithm for Multibeam Sonar
title_full_unstemmed Smart Ocean: A New Fast Deconvolved Beamforming Algorithm for Multibeam Sonar
title_short Smart Ocean: A New Fast Deconvolved Beamforming Algorithm for Multibeam Sonar
title_sort smart ocean: a new fast deconvolved beamforming algorithm for multibeam sonar
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6263529/
https://www.ncbi.nlm.nih.gov/pubmed/30453637
http://dx.doi.org/10.3390/s18114013
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