飞控与探测2026，Vol.9Issue(4)：104-111,8.DOI:10.20249/j.cnki.2096-5974.2026.04.009
基于分层特征提取的无人艇多目标航迹关联算法
A Multi-Object Track Association Algorithm for Unmanned Surface Vehicles Based on Hierarchical Feature Extraction
摘要
Abstract
Aiming at the cross-modal track association problem for unmanned surface vehicles e-quipped with navigation radar and an automatic identification system during surface ship percep-tion,this paper integrates the physical principles of tracks with the characteristics of neural net-works to propose a hierarchical feature extraction-based track association method utilizing both po-sitional and motion information.Specifically,convolutional neural networks are employed to ex-tract positional features,while a self-attention mechanism is introduced to capture critical motion features.The two types of features are then fused and fed into a long short-term memory network to achieve cross-modal track association.Experimental results demonstrate that the proposed method outperforms traditional deep learning-based association approaches on public datasets,ef-fectively improving association accuracy without significantly increasing computational overhead.关键词
航迹关联/雷达/自动识别系统/卷积神经网络/自注意力机制/长短时记忆网络Key words
track association/radar/AIS/CNN/self-attention mechanism/LSTM network分类
信息技术与安全科学引用本文复制引用
怀雪墁,郭一捷,任鹏宇,奔粤阳,李倩..基于分层特征提取的无人艇多目标航迹关联算法[J].飞控与探测,2026,9(4):104-111,8.基金项目
国家自然科学基金(52371368) （52371368）
