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Underwater Target Signal Classification Using the Hybrid Routing Neural Network
In signal analysis and processing, underwater target recognition (UTR) is one of the most important technologies. Simply and quickly identify target types using conventional methods in underwater acoustic conditions is quite a challenging task. The problem can be conveniently handled by a deep learn...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8659832/ https://www.ncbi.nlm.nih.gov/pubmed/34883803 http://dx.doi.org/10.3390/s21237799 |
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author | Cheng, Xiao Zhang, Hao |
author_facet | Cheng, Xiao Zhang, Hao |
author_sort | Cheng, Xiao |
collection | PubMed |
description | In signal analysis and processing, underwater target recognition (UTR) is one of the most important technologies. Simply and quickly identify target types using conventional methods in underwater acoustic conditions is quite a challenging task. The problem can be conveniently handled by a deep learning network (DLN), which yields better classification results than conventional methods. In this paper, a novel deep learning method with a hybrid routing network is considered, which can abstract the features of time-domain signals. The used network comprises multiple routing structures and several options for the auxiliary branch, which promotes impressive effects as a result of exchanging the learned features of different branches. The experiment shows that the used network possesses more advantages in the underwater signal classification task. |
format | Online Article Text |
id | pubmed-8659832 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-86598322021-12-10 Underwater Target Signal Classification Using the Hybrid Routing Neural Network Cheng, Xiao Zhang, Hao Sensors (Basel) Article In signal analysis and processing, underwater target recognition (UTR) is one of the most important technologies. Simply and quickly identify target types using conventional methods in underwater acoustic conditions is quite a challenging task. The problem can be conveniently handled by a deep learning network (DLN), which yields better classification results than conventional methods. In this paper, a novel deep learning method with a hybrid routing network is considered, which can abstract the features of time-domain signals. The used network comprises multiple routing structures and several options for the auxiliary branch, which promotes impressive effects as a result of exchanging the learned features of different branches. The experiment shows that the used network possesses more advantages in the underwater signal classification task. MDPI 2021-11-24 /pmc/articles/PMC8659832/ /pubmed/34883803 http://dx.doi.org/10.3390/s21237799 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Cheng, Xiao Zhang, Hao Underwater Target Signal Classification Using the Hybrid Routing Neural Network |
title | Underwater Target Signal Classification Using the Hybrid Routing Neural Network |
title_full | Underwater Target Signal Classification Using the Hybrid Routing Neural Network |
title_fullStr | Underwater Target Signal Classification Using the Hybrid Routing Neural Network |
title_full_unstemmed | Underwater Target Signal Classification Using the Hybrid Routing Neural Network |
title_short | Underwater Target Signal Classification Using the Hybrid Routing Neural Network |
title_sort | underwater target signal classification using the hybrid routing neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8659832/ https://www.ncbi.nlm.nih.gov/pubmed/34883803 http://dx.doi.org/10.3390/s21237799 |
work_keys_str_mv | AT chengxiao underwatertargetsignalclassificationusingthehybridroutingneuralnetwork AT zhanghao underwatertargetsignalclassificationusingthehybridroutingneuralnetwork |