基于注意力机制和双向GRU模型的雷达HRRP目标识别

刘家麒 陈渤 介茜

刘家麒, 陈渤, 介茜. 基于注意力机制和双向GRU模型的雷达HRRP目标识别[J]. 雷达学报, 2019, 8(5): 589–597. doi: 10.12000/JR19014
引用本文: 刘家麒, 陈渤, 介茜. 基于注意力机制和双向GRU模型的雷达HRRP目标识别[J]. 雷达学报, 2019, 8(5): 589–597. doi: 10.12000/JR19014
LIU Jiaqi, CHEN Bo, and JIE Xi. Radar high-resolution range profile target recognition based on attention mechanism and bidirectional gated recurrent[J]. Journal of Radars, 2019, 8(5): 589–597. doi: 10.12000/JR19014
Citation: LIU Jiaqi, CHEN Bo, and JIE Xi. Radar high-resolution range profile target recognition based on attention mechanism and bidirectional gated recurrent[J]. Journal of Radars, 2019, 8(5): 589–597. doi: 10.12000/JR19014

基于注意力机制和双向GRU模型的雷达HRRP目标识别

doi: 10.12000/JR19014
基金项目: 国家自然科学基金(61771361, 61701379),国家杰出青年科学基金(61525105)
详细信息
    作者简介:

    刘家麒(1994–),男,河北人,西安电子科技大学在读硕士研究生,研究方向为雷达自动目标识别、机器学习、深度学习。E-mail: jqliu_2@stu.xidian.edu.cn

    陈 渤(1979–),男,河南人,博士,教授,博士生导师,主要研究方向为机器学习、统计信号处理、雷达目标识别与检测、深度学习网络、大规模数据处理。E-mail: bchen@mail.xidian.edu.cn

    介 茜(1993–),女,陕西人,西安电子科技大学在读硕士研究生,研究方向为大数据处理与机器学习。E-mail: xjie@stu.xidian.edu.cn

    通讯作者:

    陈渤 bchen@mail.xidian.edu.cn

  • 中图分类号: TN959.1; TP183

Radar High-resolution Range Profile Target Recognition Based on Attention Mechanism and Bidirectional Gated Recurrent

Funds: The National Natural Science Foundation of China (61771361, 61701379), The National Science Fund for Distinguished Young Scholars (61525105)
More Information
  • 摘要: 针对雷达高分辨距离像(HRRP)目标识别问题,传统方法只考虑样本的包络信息而忽略了距离单元间的时序相关性,该文提出了一种基于注意力机制的双向自循环神经网络模型。该模型将时域的HRRP数据通过滑窗分为正反两个序列,并将其分别通过两个相互独立的GRU网络进行特征提取,然后将同时刻提取到的特征进行拼接,从而利用了距离像双向的时序信息。考虑到不同时刻的序列对目标分类的重要性不同,通过注意力机制自适应地对各时刻隐层特征赋予不同的权值,最后根据加权求和后的隐层特征进行目标的识别与分类。实测数据实验结果表明,该文所提方法可以有效完成高分辨距离像的目标识别问题,并且在数据发生一定的时序偏移情况下,仍然可以准确找到目标区域。

     

  • 图  1  GRU网络结构示意图

    Figure  1.  GRU network architecture

    图  2  高分辨距离像生成示意图

    Figure  2.  Illustration of an HRRP sample

    图  3  基于注意力机制的双向门控循环单元(ABi-GRU)模型结构

    Figure  3.  Structure of ABi-GRU model

    图  4  3类目标高分辨距离像的时域图

    Figure  4.  Measured HRRP examples of time domain for three airplanes

    图  5  实验数据飞行轨迹投影图

    Figure  5.  Projections of target trajectories onto the ground plane

    图  6  预处理后的测试数据与经ABi-GRU所提特征的2维PCA投影图

    Figure  6.  Visualization of test data via two-dimensional PCA

    图  7  测试数据注意力权值系数

    Figure  7.  Attention coefficients of test data

    表  1  雷达和飞机相关参数

    Table  1.   Parameters of planes and radar

    雷达参数数值飞机参数安26奖状雅克42
    中心频率5520 MHz机长(m)23.8014.4036.38
    信号带宽400 MHz机高(m)8.584.579.83
    机宽(m)29.2015.9034.88
    下载: 导出CSV

    表  2  不同方法实验识别性能对比

    Table  2.   Performance comparison with different methods

    方法MCCAGCFCNHMMAGRU-forAGRU-backBi-GRUABi-GRU
    识别性能0.5900.8520.8440.8700.8950.8560.9000.907
    下载: 导出CSV

    表  3  ABi-GRU模型对时域HRRP数据的混淆矩阵

    Table  3.   Confusion matrix of ABi-GRU model for time domain HRRP data

    分类目标分类结果
    安26奖状雅克42
    安260.91890.12700.0308
    奖状0.01460.85800.0042
    雅克420.06650.01500.9650
    平均识别性能0.9140
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-01-29
  • 修回日期:  2019-04-07
  • 网络出版日期:  2019-10-01

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