MTCL-Net: A Multi-Task Contrastive Learning Network for Underwater Acoustic Source Ranging.
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| Title: | MTCL-Net: A Multi-Task Contrastive Learning Network for Underwater Acoustic Source Ranging. |
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| Authors: | Zhao, Jixiang1,2,3 (AUTHOR), Qin, Zhiliang1,2,3,4,5 (AUTHOR) qinzhiliang@hrbeu.edu.cn, Ma, Benjun1,2,3,4 (AUTHOR), Lan, Wenjian1,2,3,4 (AUTHOR), Liu, Bingqi4,5 (AUTHOR), Pang, Shuyi1,2,3 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1343. 25p. |
| Subjects: | Contrastive learning, Acoustic localization, Deep learning, Artificial neural networks, Machine learning, Aquatic ecology |
| Abstract: | Highlights: What are the main findings? A novel multi-task contrastive learning network (MTCL-Net) is proposed for underwater acoustic source ranging, with a Siamese contrastive learning auxiliary task for source position similarity discrimination. The proposed MTCL-Net framework realizes a mean absolute error of 0.17 km and 90.36% probability of credible localization (PCL-10%) on the SWellEx-96 sea trial dataset and reaches an optimal ranging performance with only around 60% of measured samples for fine-tuning. What are the implications of the main findings? This work overcomes the strong dependence of classical underwater source localization methods on precise marine environmental parameters, providing a novel solution for passive acoustic ranging in complex and uncertain ocean environments. The contrastive learning-enhanced multi-task learning paradigm established in this study offers a generalizable research path for few-shot learning and environmental mismatch mitigation in underwater acoustic signal processing. Deep learning-based data-driven methods have gained significant attention in underwater acoustic source localization. However, their performance is often constrained by environmental disturbances and the scarcity of real-world underwater acoustic data. To address these issues, this paper presents a novel network termed MTCL-Net, a multi-task learning network that incorporates contrastive learning as an auxiliary task for underwater acoustic source ranging. A standard dataset and a perturbed dataset to simulate real underwater interferences are constructed based on known environmental parameters in this method. A Siamese dual-branch architecture is employed, where a contrastive learning task enables the automatic extraction of position-related features. The network jointly optimizes three tasks: source localization in perturbed environments, localization on the standard dataset, and position similarity discrimination, which improves the robustness and generalization ability. The experimental results on simulated and sea trial data demonstrate that MTCL-Net outperforms traditional matched field processing (MFP), single-task learning (STL), and multi-task learning based on depth–range (MTL-DR) methods in terms of mean absolute error (MAE) and probability of credible localization (PCL-10%). Specifically, on SWellEx-96 sea trial data, MTCL-Net achieves an MAE of 0.17 km and a PCL-10% of 90.36%. Moreover, the proposed method only needs a few samples for fine-tuning and shows strong practicality in uncertain marine environments. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 193715374 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: MTCL-Net: A Multi-Task Contrastive Learning Network for Underwater Acoustic Source Ranging. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhao%2C+Jixiang%22">Zhao, Jixiang</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qin%2C+Zhiliang%22">Qin, Zhiliang</searchLink><relatesTo>1,2,3,4,5</relatesTo> (AUTHOR)<i> qinzhiliang@hrbeu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Ma%2C+Benjun%22">Ma, Benjun</searchLink><relatesTo>1,2,3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lan%2C+Wenjian%22">Lan, Wenjian</searchLink><relatesTo>1,2,3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Bingqi%22">Liu, Bingqi</searchLink><relatesTo>4,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pang%2C+Shuyi%22">Pang, Shuyi</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2026, Vol. 18 Issue 9, p1343. 25p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Contrastive+learning%22">Contrastive learning</searchLink><br /><searchLink fieldCode="DE" term="%22Acoustic+localization%22">Acoustic localization</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Aquatic+ecology%22">Aquatic ecology</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? A novel multi-task contrastive learning network (MTCL-Net) is proposed for underwater acoustic source ranging, with a Siamese contrastive learning auxiliary task for source position similarity discrimination. The proposed MTCL-Net framework realizes a mean absolute error of 0.17 km and 90.36% probability of credible localization (PCL-10%) on the SWellEx-96 sea trial dataset and reaches an optimal ranging performance with only around 60% of measured samples for fine-tuning. What are the implications of the main findings? This work overcomes the strong dependence of classical underwater source localization methods on precise marine environmental parameters, providing a novel solution for passive acoustic ranging in complex and uncertain ocean environments. The contrastive learning-enhanced multi-task learning paradigm established in this study offers a generalizable research path for few-shot learning and environmental mismatch mitigation in underwater acoustic signal processing. Deep learning-based data-driven methods have gained significant attention in underwater acoustic source localization. However, their performance is often constrained by environmental disturbances and the scarcity of real-world underwater acoustic data. To address these issues, this paper presents a novel network termed MTCL-Net, a multi-task learning network that incorporates contrastive learning as an auxiliary task for underwater acoustic source ranging. A standard dataset and a perturbed dataset to simulate real underwater interferences are constructed based on known environmental parameters in this method. A Siamese dual-branch architecture is employed, where a contrastive learning task enables the automatic extraction of position-related features. The network jointly optimizes three tasks: source localization in perturbed environments, localization on the standard dataset, and position similarity discrimination, which improves the robustness and generalization ability. The experimental results on simulated and sea trial data demonstrate that MTCL-Net outperforms traditional matched field processing (MFP), single-task learning (STL), and multi-task learning based on depth–range (MTL-DR) methods in terms of mean absolute error (MAE) and probability of credible localization (PCL-10%). Specifically, on SWellEx-96 sea trial data, MTCL-Net achieves an MAE of 0.17 km and a PCL-10% of 90.36%. Moreover, the proposed method only needs a few samples for fine-tuning and shows strong practicality in uncertain marine environments. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=193715374 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18091343 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 1343 Subjects: – SubjectFull: Contrastive learning Type: general – SubjectFull: Acoustic localization Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Aquatic ecology Type: general Titles: – TitleFull: MTCL-Net: A Multi-Task Contrastive Learning Network for Underwater Acoustic Source Ranging. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhao, Jixiang – PersonEntity: Name: NameFull: Qin, Zhiliang – PersonEntity: Name: NameFull: Ma, Benjun – PersonEntity: Name: NameFull: Lan, Wenjian – PersonEntity: Name: NameFull: Liu, Bingqi – PersonEntity: Name: NameFull: Pang, Shuyi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 9 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
