电讯技术2025，Vol.65Issue(5)：663-673,11.DOI:10.20079/j.issn.1001-893x.241125004
复杂环境下基于CNN-Transformer的端到端航迹关联
End-to-End Track Association Based on CNN-Transformer in Complex Environments
摘要
Abstract
To address the challenges of track association in complex flight scenarios,an end-to-end track association algorithm based on a hybrid convolutional neural network(CNN)and Transformer network model is proposed to enhance the accuracy and efficiency of track association.Firstly,the track sets reported by two radars are grouped by target and stacked into feature pairs.Then,the normalized data is fed into the CNN-Transformer model.Spatial features are extracted and global information is processed through the CNN layers and Transformer encoder layers,followed by target association prediction.Finally,the best matching target is determined based on the prediction probabilities to output the association results.The experimental results show that the proposed algorithm improves the accuracy of track association by 15％～20％compared with traditional track association algorithms,whether in the presence of interference trajectory environments or high-density trajectory environments.Moreover,its robustness and accuracy are greatly improved in multi-target scenarios.关键词
端到端航迹关联/深度学习/卷积神经网络/TransformerKey words
end-to-end track association/deep learning/convolutional neural network/Transformer分类
电子信息工程引用本文复制引用
彭锐晖,贺基贤,孙殿星,杨雪婷..复杂环境下基于CNN-Transformer的端到端航迹关联[J].电讯技术,2025,65(5):663-673,11.基金项目
国防科技重点实验室基金项目(2023-JCJQ-LB-016) （2023-JCJQ-LB-016）
