Unmanned Surface Vehicles (USVs) have become critical tools for marine exploration, environmental monitoring, and autonomous navigation. Accurate estimation of wave direction is essential for improving USV navigation and ensuring operational safety, but traditional methods often suffer from high costs and limited spatial resolution. This paper proposes a machine learning-based approach leveraging LSTM (Long Short-Term Memory) networks to predict wave direction using sensor data collected from USVs. Experimental results show the capability of the LSTM model to learn temporal dependencies and provide accurate predictions, outperforming simpler baselines.
View on arXiv@article{habouche2025_2412.16205,
title={ Machine Learning-Based Estimation Of Wave Direction For Unmanned Surface Vehicles },
author={ Manele Ait Habouche and Mickaël Kerboeuf and Goulven Guillou and Jean-Philippe Babau },
journal={arXiv preprint arXiv:2412.16205},
year={ 2025 }
}