基于角度引导Transformer融合网络的多站协同目标识别方法

郭帅 陈婷 王鹏辉 丁军 严俊坤 王英华 刘宏伟

郭帅, 陈婷, 王鹏辉, 等. 基于角度引导Transformer融合网络的多站协同目标识别方法[J]. 雷达学报, 2023, 12(3): 516–528. doi: 10.12000/JR23014
引用本文: 郭帅, 陈婷, 王鹏辉, 等. 基于角度引导Transformer融合网络的多站协同目标识别方法[J]. 雷达学报, 2023, 12(3): 516–528. doi: 10.12000/JR23014
GUO Shuai, CHEN Ting, WANG Penghui, et al. Multistation cooperative radar target recognition based on an angle-guided transformer fusion network[J]. Journal of Radars, 2023, 12(3): 516–528. doi: 10.12000/JR23014
Citation: GUO Shuai, CHEN Ting, WANG Penghui, et al. Multistation cooperative radar target recognition based on an angle-guided transformer fusion network[J]. Journal of Radars, 2023, 12(3): 516–528. doi: 10.12000/JR23014

基于角度引导Transformer融合网络的多站协同目标识别方法

doi: 10.12000/JR23014
基金项目: 国家自然科学基金(62192714, 61701379),雷达信号处理国家级重点实验室支持计划项目(KGJ202204),中央高校基本科研业务费(QTZX22160),中国航天科技集团公司第八研究院产学研合作基金资助项目(SAST2021-011),陕西省天线与控制技术重点实验室开放基金
详细信息
    作者简介:

    郭 帅,博士生,主要研究方向为雷达目标识别

    陈 婷,博士生,主要研究方向为雷达目标识别

    王鹏辉,教授,博士生导师,主要研究方向为雷达目标识别、机器学习理论与应用研究等

    丁 军,副研究员,硕士生导师,主要研究方向为雷达目标识别、数据工程

    严俊坤,教授,博士生导师,主要研究方向为网络化雷达协同探测、雷达智能化探测等

    王英华,教授,博士生导师,主要研究方向为SAR图像目标检测与识别、极化SAR图像处理与解译等

    刘宏伟,教授,博士生导师,主要研究方向为雷达目标识别、认知雷达、网络化协同探测、雷达智能化探测等

    通讯作者:

    王鹏辉 wangpenghui@mail.xidian.edu.cn

    刘宏伟 hwliu@xidian.edu.cn

  • 责任主编:冯存前 Corresponding Editor: FENG Cunqian
  • 中图分类号: TN959

Multistation Cooperative Radar Target Recognition Based on an Angle-guided Transformer Fusion Network

Funds: The National Natural Science Foundation of China (62192714, 61701379), The Stabilization Support of National Radar Signal Processing Laboratory (KGJ202204), The Fundamental Research Funds for the Central Universities (QTZX22160), Industry-University-Research Cooperation of the 8th Research Institute of China Aerospace Science and Technology Corporation (SAST2021-011), Open Fund Shaanxi Key Laboratory of Antenna and Control Technology
More Information
  • 摘要: 多站协同雷达目标识别旨在利用多站信息的互补性提升识别性能。传统多站协同目标识别方法未直接考虑站间数据差异问题,且通常采用相对简单的融合策略,难以取得准确、稳健的识别性能。该文针对多站协同雷达高分辨距离像(HRRP)目标识别问题,提出了一种基于角度引导的Transformer融合网络。该网络以Transformer作为特征提取主体结构,提取单站HRRP的局部和全局特征。并在此基础上设计了3个新的辅助模块促进多站特征融合学习,角度引导模块、前级特征交互模块以及深层注意力特征融合模块。首先,角度引导模块使用目标方位角度对站间数据差异进行建模,强化了所提特征与多站视角的对应关系,提升了特征稳健性与一致性。其次,前级特征交互模块和深层注意力特征融合模块相结合的融合策略,实现了对各站特征的多阶段层次化融合。最后,基于实测数据模拟多站场景进行协同识别实验,结果表明所提方法能够有效地提升多站协同时的目标识别性能。

     

  • 图  1  融合网络结构示意图

    Figure  1.  Schematic of different fusion network structures

    图  2  角度引导Transformer融合网络结构

    Figure  2.  Angle guided Transformer fusion network framework

    图  3  切分映射及位置编码

    Figure  3.  Patch embedding and positional embedding

    图  4  Transformer模块

    Figure  4.  The Transformer module

    图  5  角度引导模块

    Figure  5.  The angle guided module

    图  6  前级特征交互模块

    Figure  6.  The pre-feature interaction module

    图  7  深层注意力特征融合模块

    Figure  7.  The deep attention feature fusion module

    图  8  置换不变Transformer与Transformer特征提取层对比图

    Figure  8.  Comparison of permutation invariant Transformer in feature fusion and Transformer in feature extraction

    图  9  模拟多站场景的目标HRRP

    Figure  9.  Target HRRP examples for simulating multistation scenarios

    图  10  测试集识别率混淆矩阵(%)

    Figure  10.  Confusion matrix of the recognition accuracy in test set (%)

    图  11  测试集数据与本文方法所提特征的二维t-SNE可视化

    Figure  11.  Visualization of test data and feature via two-dimensional t-SNE

    图  12  识别率和计算量随着HRRP子序列个数变化的曲线图

    Figure  12.  Accuracy and calculation amount changing with the number of HRRP subsequences

    表  1  雷达参数

    Table  1.   Parameters of radar

    参数数值
    信号带宽400 MHz
    距离分辨率0.375 m
    下载: 导出CSV

    表  2  目标物理参数

    Table  2.   Parameters of targets

    飞机型号机身长度(m)翼展宽度(m)机高(m)
    A32037.5734.1011.76
    A32144.5134.0911.76
    A330-258.8060.3017.40
    A330-363.6060.3016.85
    A35066.8064.7517.05
    下载: 导出CSV

    表  3  数据集样本分布

    Table  3.   Dataset samples distribution

    飞机型号训练样本数测试样本数
    A32026362594
    A32124822398
    A330-225562569
    A330-327852572
    A35028902181
    下载: 导出CSV

    表  4  实验参数配置

    Table  4.   Experimental parameters configuration

    实验配置参数
    训练轮次200
    批量大小64
    初始学习率1E–3
    优化器AdamW
    丢弃率0.1
    HRRP子序列的个数N8
    子序列编码维度D128
    Transformer模块数3
    注意力头数4
    角度编码全连接层输出维度(128, 1152)
    特征交互主支路权重0.6
    特征交互其余支路权重0.2
    深层注意力融合模块数1
    损失函数Cross Entropy Loss
    下载: 导出CSV

    表  5  实验结果

    Table  5.   Experimental results

    方法融合策略识别率(%)参数量(M)计算量(GFLOPs)
    CNN单站雷达181.564.190.30
    雷达287.274.190.30
    雷达390.714.190.30
    CNN多站数据融合86.354.190.30
    特征融合90.0812.591.76
    决策融合90.9612.591.76
    Transformer单站雷达187.120.890.46
    雷达288.030.890.46
    雷达393.210.890.46
    Transformer多站特征融合93.602.511.38
    本文方法方位角度引导+前级
    特征交互+深层注意
    力特征融合
    96.903.391.60
    下载: 导出CSV

    表  6  消融实验结果

    Table  6.   Results of ablation experiment

    方法角度引导前级特征
    交互
    深层注意力
    特征融合
    识别率(%)
    Transformer
    多站特征融合
    93.60
    94.50(+0.90)
    93.20(–0.40)
    93.70(+0.10)
    93.77(+0.17)
    94.47(+0.87)
    95.68(+2.08)
    本文方法96.90(+3.30)
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-02-03
  • 修回日期:  2023-04-02
  • 网络出版日期:  2023-04-23
  • 刊出日期:  2023-06-28

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