Machine Learning-Based Estimation Of Wave Direction For Unmanned Surface Vehicles

TL;DR

This paper introduces a machine learning approach using LSTM networks to accurately estimate wave direction from USV sensor data, enhancing navigation safety and operational efficiency.

Contribution

It presents a novel application of LSTM networks for wave direction estimation, demonstrating improved accuracy over traditional methods.

Findings

LSTM models outperform baseline methods in wave direction prediction.

Sensor data enables effective temporal learning for wave estimation.

The approach enhances USV navigation safety and operational performance.

Abstract

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.