# Gaussian Process Regression for Improved Underwater Navigation

arXiv: https://arxiv.org/abs/2502.16510 · HTML: https://mlsanity.com/paper/2502.16510/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 23 Feb 2025.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [LiBeamsNet: AUV Velocity Vector Estimation in Situations of Limited DVL Beam Measurements](https://mlsanity.com/paper/2210.11572/) (2022)
- [Seamless Underwater Navigation with Limited Doppler Velocity Log Measurements](https://mlsanity.com/paper/2404.13742/) (2024)
- [Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes (with Appendix)](https://mlsanity.com/paper/2309.07050/) (2023)
- [Continuum Robot State Estimation Using Gaussian Process Regression on SE(3)](https://mlsanity.com/paper/2210.14842/) (2022)

## Abstract (arXiv)

Accurate underwater navigation is a challenging task due to the absence of global navigation satellite system signals and the reliance on inertial navigation systems that suffer from drift over time. Doppler velocity logs (DVLs) are typically used to mitigate this drift through velocity measurements, which are commonly estimated using a parameter estimation approach such as least squares (LS). However, LS works under the assumption of ideal conditions and does not account for sensor biases, leading to suboptimal performance. This paper proposes a data-driven alternative based on multi-output Gaussian process regression (MOGPR) to improve DVL velocity estimation. MOGPR provides velocity estimates and associated measurement covariances, enabling an adaptive integration within an error-state Extended Kalman Filter (EKF). We evaluate our proposed approach using real-world AUV data and compare it against LS and a state-of-the-art deep learning model, BeamsNet. Results demonstrate that MOGPR reduces velocity estimation errors by approximately 20% while simultaneously enhancing overall navigation accuracy, particularly in the orientation states. Additionally, the incorporation of uncertainty estimates from MOGPR enables an adaptive EKF framework, improving navigation robustness in dynamic underwater environments.