Data-Driven Strategies for Coping with Incomplete DVL Measurements

• URL: http://arxiv.org/abs/2401.15620v1
• Date: Sun, 28 Jan 2024 10:17:36 GMT
• Title: Data-Driven Strategies for Coping with Incomplete DVL Measurements
• Authors: Nadav Cohen and Itzik Klein
• Abstract summary: In real-world scenarios, incomplete Doppler velocity log measurements occur, resulting in positioning errors and mission aborts. This paper presents a comparative analysis of two cutting-edge deep learning methodologies, namely LiBeamsNet and MissBeamNet. We found that both deep learning architectures outperformed model-based approaches by over 16% in velocity prediction accuracy.
• Score: 15.619053656143564
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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