Data-Driven Strategies for Coping with Incomplete DVL Measurements
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913536243924992 |
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| author | Cohen, Nadav Klein, Itzik |
| author_facet | Cohen, Nadav Klein, Itzik |
| contents | Autonomous underwater vehicles are specialized platforms engineered for deep underwater operations. Critical to their functionality is autonomous navigation, typically relying on an inertial navigation system and a Doppler velocity log. In real-world scenarios, incomplete Doppler velocity log measurements occur, resulting in positioning errors and mission aborts. To cope with such situations, a model and learning approaches were derived. This paper presents a comparative analysis of two cutting-edge deep learning methodologies, namely LiBeamsNet and MissBeamNet, alongside a model-based average estimator. These approaches are evaluated for their efficacy in regressing missing Doppler velocity log beams when two beams are unavailable. In our study, we used data recorded by a DVL mounted on an autonomous underwater vehicle operated in the Mediterranean Sea. We found that both deep learning architectures outperformed model-based approaches by over 16% in velocity prediction accuracy. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_15620 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data-Driven Strategies for Coping with Incomplete DVL Measurements Cohen, Nadav Klein, Itzik Robotics Artificial Intelligence Systems and Control Signal Processing Autonomous underwater vehicles are specialized platforms engineered for deep underwater operations. Critical to their functionality is autonomous navigation, typically relying on an inertial navigation system and a Doppler velocity log. In real-world scenarios, incomplete Doppler velocity log measurements occur, resulting in positioning errors and mission aborts. To cope with such situations, a model and learning approaches were derived. This paper presents a comparative analysis of two cutting-edge deep learning methodologies, namely LiBeamsNet and MissBeamNet, alongside a model-based average estimator. These approaches are evaluated for their efficacy in regressing missing Doppler velocity log beams when two beams are unavailable. In our study, we used data recorded by a DVL mounted on an autonomous underwater vehicle operated in the Mediterranean Sea. We found that both deep learning architectures outperformed model-based approaches by over 16% in velocity prediction accuracy. |
| title | Data-Driven Strategies for Coping with Incomplete DVL Measurements |
| topic | Robotics Artificial Intelligence Systems and Control Signal Processing |
| url | https://arxiv.org/abs/2401.15620 |