基于“数据驱动+智能学习”的合成孔径雷达学习成像

罗迎 倪嘉成 张群

罗迎, 倪嘉成, 张群. 基于“数据驱动+智能学习”的合成孔径雷达学习成像[J]. 雷达学报, 2020, 9(1): 107–122. doi: 10.12000/JR19103
引用本文: 罗迎, 倪嘉成, 张群. 基于“数据驱动+智能学习”的合成孔径雷达学习成像[J]. 雷达学报, 2020, 9(1): 107–122. doi: 10.12000/JR19103
LUO Ying, NI Jiacheng, and ZHANG Qun. Synthetic aperture radar learning-imaging method based on data-driven technique and artificial intelligence[J]. Journal of Radars, 2020, 9(1): 107–122. doi: 10.12000/JR19103
Citation: LUO Ying, NI Jiacheng, and ZHANG Qun. Synthetic aperture radar learning-imaging method based on data-driven technique and artificial intelligence[J]. Journal of Radars, 2020, 9(1): 107–122. doi: 10.12000/JR19103

基于“数据驱动+智能学习”的合成孔径雷达学习成像

doi: 10.12000/JR19103
基金项目: 国家自然科学基金(61631019, 61971434)
详细信息
    作者简介:

    罗 迎(1984–),男,湖南益阳人,空军工程大学信息与导航学院副教授,博士生导师,主要研究方向为雷达成像与目标识别。E-mail: luoying2002521@163.com

    倪嘉成(1990–),男,陕西西安人,空军工程大学信息与导航学院讲师,主要研究方向为SAR成像与目标识别。E-mail: littlenjc@sina.com

    张 群(1964–),男,陕西合阳人,空军工程大学信息与导航学院教授,博士生导师,主要研究方向为雷达成像与目标识别。E-mail: zhangqunnus@gmail.com

    通讯作者:

    罗迎 luoying2002521@163.com

  • 中图分类号: TN957.5

Synthetic Aperture Radar Learning-imaging Method Based onData-driven Technique and Artificial Intelligence

Funds: The National Natural Science Foundation of China (61631019, 61971434)
More Information
  • 摘要: 对感兴趣目标的数量、位置、型号等参数信息的精确获取一直是合成孔径雷达(SAR)技术中最为重要的研究内容之一。现阶段的SAR信息处理主要分为成像和解译两大部分,两者的研究相对独立。SAR成像和解译各自开发了大量算法,复杂度越来越高,但SAR解译并未因成像分辨率提升而变得简单,特别是对重点目标识别率低的问题并未从本质上得以解决。针对上述问题,该文从SAR成像解译一体化角度出发,尝试利用“数据驱动+智能学习”的方法提升机载SAR的信息处理能力。首先分析了基于“数据驱动+智能学习”方法的SAR成像解译一体化的可行性及现阶段存在的主要问题;在此基础上,提出一种“数据驱动+智能学习”的SAR学习成像方法,给出了学习成像框架、网络参数选取方法、网络训练方法和初步的仿真结果,并分析了需要解决的关键性技术问题。

     

  • 图  1  现阶段SAR信息处理基本流程图

    Figure  1.  Basic flow chart of SAR information processing at present

    图  2  一种SAR“回波数据域”到“目标参数域”成像解译一体化流程图

    Figure  2.  An flow chart of SAR imaging & interpretation integration from “echo data domain” to “target parameter domain”

    图  3  1维SAR观测模型示意图

    Figure  3.  One dimensional SAR observation model

    图  4  SAR学习成像网络结构图

    Figure  4.  Network structure of SAR learning-imaging

    图  5  非监督训练、γ=1, 20 dB信噪比条件下成像结果对比

    Figure  5.  Comparison of imaging results using unsupervised training, γ=1 and SNR=20 dB

    图  6  非监督训练、γ=0.1, 5 dB信噪比条件下成像结果对比

    Figure  6.  Comparison of imaging results using unsupervised training, γ=0.1 and SNR=5 dB

    图  7  低PRF条件下成像结果对比

    Figure  7.  Imaging results under low PRF condition

    图  8  不同样本数据量对应的网络训练误差

    Figure  8.  Network training error corresponding to differentnumber of training samples

    图  9  含载机轨迹误差条件下成像结果对比

    Figure  9.  Imaging results with platform trajectory error

    图  10  MSTAR目标成像结果对比

    Figure  10.  MSTAR target imaging results

    图  11  MSTAR其它两种目标成像结果对比

    Figure  11.  Imaging results of two different targets in MSTAR dataset

    表  1  雷达参数和网络参数

    Table  1.   Parameters of radar and network

    参数
    雷达参数载频fc (GHz)10
    带宽Br (MHz)150
    脉冲重复频率PRF (Hz)500
    脉冲持续时间Tr (μs)1.2
    方位向分辨率(m)2
    距离向分辨率(m)2
    平台高度H (m)10000
    平台速度v (m/s)100
    网络参数网络层数L
    训练样本数量Ntrain
    学习率η
    随机降采样率γ
    下载: 导出CSV

    表  2  成像质量与成像时间对比

    Table  2.   Comparison of imaging quality and imaging time

    算法γ=1, 20 dB信噪比γ=0.1, 5 dB信噪比
    PSNR (dB)NMSEPLSR (dB)成像时间(s) PSNR (dB)NMSEPLSR (dB)成像时间(s)
    stOMP24.610.25–15.5510.93–13.051.46e31.20
    l1/2范数ISTA24.440.26–15.6948.3421.600.50–13.874.83
    所提方法L=319.950.73–17.400.15119.720.77–13.560.012
    所提方法L=825.580.20–19.490.15422.910.37–16.010.012
    所提方法L=1125.800.19–19.520.15122.910.37–16.130.012
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-11-27
  • 修回日期:  2020-02-26
  • 网络出版日期:  2020-02-01

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