Data-Driven Strategies for Coping with Incomplete DVL Measurements
CoRR(2024)
摘要
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
velocity prediction accuracy.
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