Machine Learning-Based Estimation Of Wave Direction For Unmanned Surface Vehicles
This addresses the need for cost-effective and high-resolution wave direction estimation for USV operators in marine applications, but it is incremental as it applies an existing LSTM method to this specific domain.
The paper tackled the problem of accurately estimating wave direction for Unmanned Surface Vehicles (USVs) to improve navigation and safety, and the result showed that an LSTM-based machine learning approach outperformed simpler baselines in predictions.
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.