指挥控制与仿真2025，Vol.47Issue(2)：75-86,12.DOI:10.3969/j.issn.1673-3819.2025.02.010
基于深度学习网络的航迹分层分类研究
Research on trajectory hierarchical classification based on deep learning networks
摘要
Abstract
In response to the issue that existing trajectory classification methods fail to fully consider the time series features and spatial structure features of trajectories,leading to a decline in classification accuracy,this paper proposes a trajectory hierarchical classification method based on deep learning networks.First,ship trajectories are transformed into image layers,and a trajectory image layer classification model based on the Swin-Transformer network is constructed.Next,for the trajecto-ry sequence layer,a multi-dimensional information-based trajectory compression algorithm is utilized to optimize the input of trajectory sequences,and a trajectory sequence layer classification model based on the Gained-Transformer-Network deep learning network is developed.At last,a confidence-based fusion layer trajectory classification model is established to im-prove the accuracy of layered trajectory classification.Experimental validation shows an average classification accuracy of 90%,with the performance of the ensemble classifier improving by an average of 11%compared to other single classifiers,and an average F1 score of 0.82.The results indicate that the newly proposed method and the new ensemble classifier dem-onstrate good classification effectiveness for ship trajectories.关键词
航迹分类/深度学习/Swin-Transformer/Gained-Transformer-Network/分层分类Key words
trajectory classification/deep learning network/swin-transformer/gained-transformer-network/hierarchical classification分类
信息技术与安全科学引用本文复制引用
王伊凡,吉琳娜,杨风暴..基于深度学习网络的航迹分层分类研究[J].指挥控制与仿真,2025,47(2):75-86,12.
