文献类型：期刊文献

英文题名：Improved deep learning method and high-resolution reanalysis model-based intelligent marine navigation

作者：Zhang, Zeguo[1,2,3];Cao, Liang[1,2,3];Yin, Jianchuan[1,2,3]

机构：[1]Guangdong Ocean Univ, Naval Architecture & Shipping Coll, Zhanjiang, Peoples R China;[2]Guangdong Prov Key Lab Intelligent Equipment South, Zhanjiang, Peoples R China;[3]Guangdong Prov Engn Res Ctr Ship Intelligence & Sa, Zhanjiang, Peoples R China

年份：2025

卷号：12

外文期刊名：FRONTIERS IN MARINE SCIENCE

收录：SCI-EXPANDED(收录号：WOS:001476390600001)、、Scopus(收录号：2-s2.0-105003802175)、WOS

基金：The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by the National Natural Science Foundation of China under Grants 52271361 and 52231014, the Special Projects of Key Areas for Colleges and Universities in Guangdong Province under Grant 2021ZDZX1008, the Natural Science Foundation of Guangdong Province of China under Grant 2023A1515010684.

语种：英文

外文关键词：extreme wind forecast; machine learning; marine navigation; incremental principal component analysis; depthwise-separable convolution

外文摘要：Large-scale weather forecasting is critical for ensuring maritime safety and optimizing transoceanic voyages. However, sparse meteorological data, incomplete forecasts, and unreliable communication hinder accurate, high-resolution wind system predictions. This study addresses these challenges to enhance dynamic voyage planning and intelligent ship navigation. We propose IPCA-MHA-DSRU-Net, a novel deep learning model integrating incremental principal component analysis (IPCA) with a spatial-temporal depthwise separable U-Net. Key components include: (1) IPCA preprocessing to reduce dimensionality and noise in 2D wind field data; (2) depthwise-separable convolution (DSC) blocks to minimize parameters and computational costs; (3) multi-head attention (MHA) and residual mechanisms to improve spatial-temporal feature extraction and prediction accuracy. The framework is optimized for real-time onboard deployment under communication constraints. The model achieves high accuracy in high-resolution wind predictions, validated through reanalysis datasets. Experiments demonstrated enhanced path planning efficiency and robustness in dynamic oceanic conditions. The IPCA-MHA-DSRU-Net balances computational efficiency and accuracy, making it viable for resource-limited ships. This novel IPCA application provides a promising alternative for preprocessing large-scale meteorological data.