Navigating the Depths: A Comprehensive Survey of Deep Learning for Passive Underwater Acoustic Target Recognition
The field of deep learning is a rapidly developing research area with numerous applications across multiple domains. Sonar (SOund Navigation And Ranging) processing has traditionally been a field of statistical analysis. However, in the past ten to fifteen years, the rapid growth of deep learning ha...
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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10716649/ |
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| author | Nils Muller Jens Reermann Tobias Meisen |
| author_facet | Nils Muller Jens Reermann Tobias Meisen |
| author_sort | Nils Muller |
| collection | DOAJ |
| description | The field of deep learning is a rapidly developing research area with numerous applications across multiple domains. Sonar (SOund Navigation And Ranging) processing has traditionally been a field of statistical analysis. However, in the past ten to fifteen years, the rapid growth of deep learning has challenged classical approaches with modern deep learning-based methods. This survey provides a systematic overview of the Underwater Acoustic Target Recognition (UATR) domain within the area of deep learning. The objective is to highlight popular design choices and evaluate the commonalities and differences of the investigated techniques in relation to the selected architectures and pre-processing methods. Furthermore, this survey examines the state of UATR literature through the identification of prominent conferences and journals which points new researchers in directions where to allocate UATR related publications. Additionally, popular datasets and available benchmarks are identified and analysed for complexity coverage. This work targets researchers new to the field as well as experienced researchers that want to get a broader overview. Nonetheless, experienced sonar engineers with a strong background within classical analysis also benefit from this survey. |
| format | Article |
| id | doaj-art-7e9f5c52638f4a3fa8da3fe5bb3f4935 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-7e9f5c52638f4a3fa8da3fe5bb3f49352025-08-20T02:12:02ZengIEEEIEEE Access2169-35362024-01-011215409215411810.1109/ACCESS.2024.348078810716649Navigating the Depths: A Comprehensive Survey of Deep Learning for Passive Underwater Acoustic Target RecognitionNils Muller0https://orcid.org/0009-0000-6627-7019Jens Reermann1https://orcid.org/0000-0002-0467-0947Tobias Meisen2https://orcid.org/0000-0002-1969-559XATLAS ELEKTRONIK GmbH, Bremen, GermanyATLAS ELEKTRONIK GmbH, Bremen, GermanyInstitute for Technologies and Management of Digital Transformation, Bergische Universität Wuppertal, Wuppertal, GermanyThe field of deep learning is a rapidly developing research area with numerous applications across multiple domains. Sonar (SOund Navigation And Ranging) processing has traditionally been a field of statistical analysis. However, in the past ten to fifteen years, the rapid growth of deep learning has challenged classical approaches with modern deep learning-based methods. This survey provides a systematic overview of the Underwater Acoustic Target Recognition (UATR) domain within the area of deep learning. The objective is to highlight popular design choices and evaluate the commonalities and differences of the investigated techniques in relation to the selected architectures and pre-processing methods. Furthermore, this survey examines the state of UATR literature through the identification of prominent conferences and journals which points new researchers in directions where to allocate UATR related publications. Additionally, popular datasets and available benchmarks are identified and analysed for complexity coverage. This work targets researchers new to the field as well as experienced researchers that want to get a broader overview. Nonetheless, experienced sonar engineers with a strong background within classical analysis also benefit from this survey.https://ieeexplore.ieee.org/document/10716649/Deep learningpassive sonar classificationunderwater acoustic target recognition |
| spellingShingle | Nils Muller Jens Reermann Tobias Meisen Navigating the Depths: A Comprehensive Survey of Deep Learning for Passive Underwater Acoustic Target Recognition IEEE Access Deep learning passive sonar classification underwater acoustic target recognition |
| title | Navigating the Depths: A Comprehensive Survey of Deep Learning for Passive Underwater Acoustic Target Recognition |
| title_full | Navigating the Depths: A Comprehensive Survey of Deep Learning for Passive Underwater Acoustic Target Recognition |
| title_fullStr | Navigating the Depths: A Comprehensive Survey of Deep Learning for Passive Underwater Acoustic Target Recognition |
| title_full_unstemmed | Navigating the Depths: A Comprehensive Survey of Deep Learning for Passive Underwater Acoustic Target Recognition |
| title_short | Navigating the Depths: A Comprehensive Survey of Deep Learning for Passive Underwater Acoustic Target Recognition |
| title_sort | navigating the depths a comprehensive survey of deep learning for passive underwater acoustic target recognition |
| topic | Deep learning passive sonar classification underwater acoustic target recognition |
| url | https://ieeexplore.ieee.org/document/10716649/ |
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