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基于深度多尺度一维卷积神经网络的雷达舰船目标识别

郭晨 简涛 徐从安 何友 孙顺

郭晨, 简涛, 徐从安, 何友, 孙顺. 基于深度多尺度一维卷积神经网络的雷达舰船目标识别[J]. 电子与信息学报, 2019, 41(6): 1302-1309. doi: 10.11999/JEIT180677
引用本文: 郭晨, 简涛, 徐从安, 何友, 孙顺. 基于深度多尺度一维卷积神经网络的雷达舰船目标识别[J]. 电子与信息学报, 2019, 41(6): 1302-1309. doi: 10.11999/JEIT180677
Chen GUO, Tao JIAN, Congan XU, You HE, Shun SUN. Radar HRRP Target Recognition Based on Deep Multi-Scale 1D Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2019, 41(6): 1302-1309. doi: 10.11999/JEIT180677
Citation: Chen GUO, Tao JIAN, Congan XU, You HE, Shun SUN. Radar HRRP Target Recognition Based on Deep Multi-Scale 1D Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2019, 41(6): 1302-1309. doi: 10.11999/JEIT180677

基于深度多尺度一维卷积神经网络的雷达舰船目标识别

doi: 10.11999/JEIT180677
基金项目: 国家自然科学基金(61471379, 61790551, 61102166),泰山学者工程专项
详细信息
    作者简介:

    郭晨:女,1990年生,博士生,研究方向为雷达目标识别、深度学习

    简涛:男,1980年生,副教授,研究方向为雷达信号处理、目标识别

    徐从安:男,1987年生,讲师,研究方向为多目标跟踪、信息融合、深度学习

    何友:男,1956年生,教授,研究方向为信息融合、军事大数据

    孙顺:男,1992年生,博士生,研究方向为信息融合、无源定位、协同控制

    通讯作者:

    简涛 work_jt@163.com

  • 中图分类号: TN957.51

Radar HRRP Target Recognition Based on Deep Multi-Scale 1D Convolutional Neural Network

Funds: The National Natural Science Foundation of China (61471379, 61790551, 61102166), The Taishan Scholar Project of Shandong Province
  • 摘要: 为满足雷达舰船目标识别的高实时性和高泛化性的需求,该文提出了一种基于深度多尺度1维卷积神经网络的目标高分辨1维距离像(HRRP)识别方法。针对高分辨1维距离像特征提取难的问题,所提方法通过共享卷积核的权值,使用多尺度的卷积核提取不同精细度的特征,并构造中心损失函数来提高特征的分辨能力。实验结果表明,该模型可以显著提高目标在非理想条件下的识别正确率,克服目标姿态角敏感性问题,具有良好的鲁棒性和泛化性。
  • 图  1  本文所提模型示意图

    图  2  多尺度卷积层示意图

    图  3  多尺度降采样层示意图

    图  4  CNN与MSCNN在不同损失函数条件下的特征可视化

    表  1  所提模型中主要的特征提取层的参数个数

    卷积层多尺度卷积层1多尺度下采样层1多尺度卷积层2多尺度下采样层2多尺度卷积层3全连接层合计
    2415524480601644805760256024872
    下载: 导出CSV

    表  2  CNN中主要的特征提取层的参数个数

    卷积层32, 5×1卷积层32, 5×1卷积层64, 3×1卷积层64, 3×1卷积层128, 1×1卷积层128, 1×1全连接层2合计
    1605120614412288819216384204850336
    下载: 导出CSV

    表  3  自编码模型中主要的特征提取层的参数个数

    隐藏层1, 600隐藏层2, 300隐藏层3, 50合计
    30720018000015000502200
    下载: 导出CSV

    表  4  7种舰船目标的结构参数(m)

    舰船编号舰长舰宽吃水深度
    1182.824.18.1
    2172.816.86.5
    3153.820.46.3
    4135.016.84.5
    5121.017.64.3
    6102.216.54.2
    789.312.14.0
    下载: 导出CSV

    表  5  不同模型深度条件下的识别正确率(%)

    模型识别正确率
    本文模型97.67
    模型a89.23
    模型b82.41
    模型c75.25
    下载: 导出CSV

    表  6  所提模型与对比模型在不同信噪比条件下的目标识别正确率(%)

    模型名称SNR (dB)
    51015
    本文模型95.1297.6798.90
    CNN+CL93.8895.8997.56
    SDAE+MLP90.5892.1593.22
    SAE+ELM90.9492.6394.05
    下载: 导出CSV

    表  7  所提模型与对比模型在数据集B下的目标识别正确率(%)

    本文模型CNN+CLSDAE+MLPSAE+ELM
    94.8393.5190.9391.27
    下载: 导出CSV
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    FENG Bo, CHEN Bo, WANG Penghui, et al. Radar high resolution range profile target recognition algorithm via stable dictionary learning[J]. Journal of Electronics &Information Technology, 2015, 37(6): 1457–1462. doi: 10.11999/JEIT141227
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出版历程
  • 收稿日期:  2018-07-06
  • 修回日期:  2019-01-10
  • 网络出版日期:  2019-01-22
  • 刊出日期:  2019-06-01

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