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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18091343 |
