舰船电子工程2026，Vol.46Issue(6)：49-53,5.DOI:10.3969/j.issn.1672-9730.2026.06.008
基于二维卷积和Transformer的船舶轨迹预测方法
A Ship Trajectory Prediction Method Based on 2D Convolution and Transformer
万民 1邹世军 2郭小宇2
作者信息
- 1. 91404部队 秦皇岛 066000
- 2. 武汉船舶通信研究所 武汉 430200
- 折叠
摘要
Abstract
This paper proposes a 2D-CNN-Transformer model for ship trajectory prediction,integrating 2D convolution neu-ral networks with Transformer architecture to address the limitations of discrete position encoding and single modal feature extraction in conventional methods.The model employs continuous position encoding to eliminate quantization errors induced by grid-based co-ordinate mapping,reconstructs trajectory images for spatial feature extraction via ResNet,and leverages cross-attention for dynam-ic multi-modal feature fusion.Experimental results demonstrate superior performance in mean square error(MSE)and root mean square error(RMSE)across 5 min,10 min,and 15 min prediction horizons compared to baseline models.The synergistic integra-tion of continuous position encoding,two-dimensional convolutional branches,and cross-attention mechanisms offers a novel para-digm for high-precision trajectory prediction within intelligent shipping systems.关键词
Transformer/卷积神经网络/船舶轨迹预测/交叉注意力Key words
Transformer/convolution neural network/ship trajectory prediction/cross attention分类
信息技术与安全科学引用本文复制引用
万民,邹世军,郭小宇..基于二维卷积和Transformer的船舶轨迹预测方法[J].舰船电子工程,2026,46(6):49-53,5.
