用LSTM模型从传感器数据预测波向，提升无人船导航安全
Machine Learning-Based Estimation Of Wave Direction For Unmanned Surface Vehicles
- 用LSTM网络学习波浪数据的时间依赖关系
- 模型预测精度优于传统简单方法
- 适合无人船自主航行与海洋监测场景
无人水面艇（USVs）已成为海洋勘探、环境监测和自主导航的关键工具。准确估计波向对提升USV导航性能和保障作业安全至关重要，但传统方法常面临成本高和空间分辨率有限的问题。本文提出一种基于机器学习的方法，利用长短期记忆（LSTM）网络，通过USV采集的传感器数据预测波向。实验结果表明，该LSTM模型能够有效学习时间依赖性，提供高精度预测，显著优于简单基线模型。
原文摘要 · Abstract (English)
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.
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