# Data-Driven Strategies for Coping with Incomplete DVL Measurements

arXiv: https://arxiv.org/abs/2401.15620 · HTML: https://mlsanity.com/paper/2401.15620/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 28 Jan 2024.

## Builds on (most cited first, 7 of 7)

- [Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions](https://mlsanity.com/paper/2307.00014/) (2023)
- [BeamsNet: A data-driven Approach Enhancing Doppler Velocity Log Measurements for Autonomous Underwater Vehicle Navigation](https://mlsanity.com/paper/2206.13603/) (2022)
- [Adaptive Kalman-Informed Transformer](https://mlsanity.com/paper/2401.09987/) (2024)
- [Set-Transformer BeamsNet for AUV Velocity Forecasting in Complete DVL Outage Scenarios](https://mlsanity.com/paper/2212.11671/) (2022)
- [LiBeamsNet: AUV Velocity Vector Estimation in Situations of Limited DVL Beam Measurements](https://mlsanity.com/paper/2210.11572/) (2022)
- [MissBeamNet: Learning Missing Doppler Velocity Log Beam Measurements](https://mlsanity.com/paper/2301.11597/) (2023)
- [A Hybrid Adaptive Velocity Aided Navigation Filter with Application to INS/DVL Fusion](https://mlsanity.com/paper/2211.01329/) (2022)

## Built on by (most cited first, 1 of 1)

- [Seamless Underwater Navigation with Limited Doppler Velocity Log Measurements](https://mlsanity.com/paper/2404.13742/) (2024)

## Abstract (arXiv)

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.