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.

翻译：自主水下航行器是专为深水作业设计的专用平台。其核心功能在于自主导航，通常依赖惯性导航系统与多普勒测速仪实现。在实际场景中，多普勒测速仪会出现不完整测量结果，导致定位误差和任务中断。为应对此类情况，本文推导了模型与学习方法，并对两种前沿深度学习方法（LiBeamsNet与MissBeamNet）以及基于模型的平均估计器进行了比较分析。这些方法在缺失两个波束时回归缺失DVL波束的有效性得到了评估。研究中使用了一台部署于地中海的自主水下航行器搭载的DVL所记录的数据。结果表明，两种深度学习架构在速度预测精度上均比基于模型的方法提升超过16%。