Data-Driven Strategies for Coping with Incomplete DVL Measurements
This work addresses navigation reliability for autonomous underwater vehicles in real-world scenarios, but it appears incremental as it compares existing deep learning methods against a baseline.
The paper tackled the problem of incomplete Doppler velocity log measurements in autonomous underwater vehicles, which cause positioning errors, by comparing deep learning methods (LiBeamsNet and MissBeamNet) with a model-based estimator, finding that the deep learning approaches improved velocity prediction accuracy by over 16%.
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.