现代雷达2026，Vol.48Issue(6)：10-19,10.DOI:10.16592/j.cnki.1004-7859.20240923002
融合全局注意力机制和时空特征的ARPA雷达海上目标分类方法
A Maritime Target Classification Method for ARPA Radar Fusing Global Attention Mechanism with Spatiotemporal Features
摘要
Abstract
To address the limitation of traditional maritime target classification methods in extracting key spatiotemporal features from automatic radar plotting aid(ARPA)radar trajectory data,a maritime target classification method for ARPA radar fusing global attention mechanism and spatiotemporal features is proposed in this paper.First,aiming at the insufficient extraction of traj-ectory spatial features caused by the single-channel input structure,a trajectory spatial feature extraction method based on multi-channel input one-dimensional convolutional neural network is proposed to realize the effective extraction of trajectory spatial fea-tures.Then,to solve the difficulty of capturing long-term dependent information of trajectory data in the long short-term memory(LSTM)network,a trajectory temporal feature extraction method based on bidirectional LSTM is presented,which can effectively extract trajectory features over long time intervals in both past and future periods.Finally,in view of the problem that key features are weakened due to equal weight assignment when fusing spatial and temporal features,a global attention-based spatiotemporal feature fusion strategy is proposed to achieve dynamic fusion according to feature importance.Experiments are conducted using real ARPA data to verify the effectiveness of the proposed method.The experimental results show that the classification accuracy of the proposed method reaches 97.45%on the real ARPA dataset.Compared with three other commonly used classification methods,the accuracy of the proposed method is improved by 9.77%,5.75%and 2.20%,respectively.The research indicates that the pro-posed method can effectively solve the problem of maritime target classification and achieve precise classification of maritime targets using ARPA radar.关键词
目标分类/一维卷积神经网络/双向长短期记忆网络/全局注意力机制/自动雷达标绘仪Key words
target classification/one-dimensional convolutional neural network(1D CNN)/bidirectional long short-term memory network(BiLSTM)/global attention mechanism/automatic radar plotting aid(ARPA)分类
交通工程引用本文复制引用
张志涵,于红,程志澳,刘茜平,崔智博,刘明剑..融合全局注意力机制和时空特征的ARPA雷达海上目标分类方法[J].现代雷达,2026,48(6):10-19,10.基金项目
国家自然科学基金资助项目(31972846) （31972846）
辽宁省专项资金资助项目(2024JBQNZ007) （2024JBQNZ007）
辽宁省教育厅基本科研资助项目(LJ212410158018) （LJ212410158018）
