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.

比较分析了 LiBeamsNet 和 MissBeamNet 这两种先进的深度学习方法以及基于模型的平均估计器的有效性，这些方法在两个波束不可用时对于回归丢失的 Doppler 速度日志波束进行了评估。研究发现，这两种深度学习架构的速度预测准确性相比基于模型的方法提高了 16% 以上。