基于仿真样本迁移学习的穿墙雷达高分辨成像方法

陈一凡 刘剑刚 贾勇 郭世盛 崔国龙

陈一凡, 刘剑刚, 贾勇, 等. 基于仿真样本迁移学习的穿墙雷达高分辨成像方法[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR24049
引用本文: 陈一凡, 刘剑刚, 贾勇, 等. 基于仿真样本迁移学习的穿墙雷达高分辨成像方法[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR24049
CHEN Yifan, LIU Jiangang, JIA Yong, et al. High-resolution imaging method for a through-the-wall radar based on transfer learning with simulation samples[J]. Journal of Radars, in press. doi: 10.12000/JR24049
Citation: CHEN Yifan, LIU Jiangang, JIA Yong, et al. High-resolution imaging method for a through-the-wall radar based on transfer learning with simulation samples[J]. Journal of Radars, in press. doi: 10.12000/JR24049

基于仿真样本迁移学习的穿墙雷达高分辨成像方法

doi: 10.12000/JR24049
基金项目: 四川省科技厅计划项目(2022YFS0531),国家自然科学基金(62001091), 衢州市政府资助项目(2022D008, 2022D005)
详细信息
    作者简介:

    陈一凡,硕士生,主要研究方向为穿墙雷达探测、智能信号处理

    刘剑刚,博士,主要研究方向为穿墙雷达探测、毫米波雷达感知

    贾 勇,博士,硕士生导师,主要研究方向为城市环境遮蔽目标探测、毫米波雷达感知

    郭世盛,博士,硕士生导师,主要研究方向为城市环境遮蔽目标探测、毫米波雷达感知

    崔国龙,博士,博士生导师,主要研究方向为认知雷达系统与信号处理、MIMO雷达、城市环境遮蔽目标探测技术与系统、阵列信号处理、认知电磁对抗技术、多功能一体化技术

    通讯作者:

    刘剑刚 jgliu@csj.uestc.edu.cn

    贾勇 jiayong2014@cdut.edu.cn

  • 责任主编:叶盛波 Corresponding Editor: YE Shengbo
  • 中图分类号: TN958

High-resolution Imaging Method for a Through-the-wall Radar Based on Transfer Learning with Simulation Samples

Funds: The Sichuan Science and Technology Program (2022YFS0531), The National Natural Science Foundation of China (62001091), The Municipal Government of Quzhou (2022D008, 2022D005)
More Information
  • 摘要: 针对带标注实测样本受限情况下的遮蔽多目标高分辨成像问题,提出一种基于迁移学习的穿墙雷达成像方法。首先,搭建生成对抗子网络实现带标签仿真数据到实测数据的迁移,解决带标签数据制作困难的问题。然后,联合使用注意力机制、自适应残差块及多尺度判别器提高图像迁移质量,引入结构一致性损失函数减小图像间的感知差异;最后,利用带标签数据训练穿墙雷达目标成像子网络,实现穿墙雷达多目标高分辨成像。实验结果表明,所提方法能有效缩小仿真图像和实测图像域间差异,实现穿墙雷达带标签伪实测图像生成,系统性解决了穿墙雷达遮蔽目标成像面临的旁/栅瓣鬼影干扰、目标图像散焦、多目标互扰等问题,在单、双和三目标场景下成像准确率分别达到98.24%, 90.97%和55.17%,相比于传统CycleGAN方法,所提方法成像准确率分别提升了2.29%, 40.28%和15.51%。

     

  • 图  1  MIMO穿墙成像雷达模型

    Figure  1.  A simple of Multiple-Input Multiple-Output (MIMO) Through-the-Wall Radar Imaging (TWRI)

    图  2  穿墙雷达成像结果

    Figure  2.  Result of MIMO TWRI

    图  3  模型框架示意图

    Figure  3.  Schematic diagram of the model framework

    图  4  域自适应模型框架示意图

    Figure  4.  Schematic diagram of the domain adaptation model framework

    图  5  带CAM类激活图的自适应残差块

    Figure  5.  Adaptive residual block with CAM class activation map

    图  6  多尺度判别器训练流程

    Figure  6.  Multi-scale discriminator training process

    图  7  Patch-GAN判别器结构

    Figure  7.  Discriminator structure of Patch-GAN

    图  8  穿墙雷达仿真图像

    Figure  8.  Simulation images of Through-the-Wall Radar (TWR)

    图  9  穿墙雷达探测场景

    Figure  9.  TWR detection scene

    图  10  穿墙雷达实测图像

    Figure  10.  TWR measured image

    图  11  迁移学习结果(黄色圆圈标记生成图像纹理差异部分)

    Figure  11.  Transfer learning results (yellow circle marking generates texture differences in the image)

    图  12  训练过程中图像FID得分(虚线为训练过程中实际FID得分曲线,实线表示经平滑处理后的FID得分曲线)

    Figure  12.  FID Score during training (the dotted line is the actual FID score curve during the training process, and the solid line represents the FID score curve after smoothing)

    图  13  不同方法训练目标成像网络的成像测试结果(图中大方框图像是小方框的放大图像)

    Figure  13.  Target imaging results of different method (the large box image in the figure is an enlarged image of the small box)

    表  1  仿真参数设置

    Table  1.   Parameter setting

    参数 取值 参数 取值
    中心频率f 1.5 GHz 墙体电导率${\sigma _{\mathrm{w}}}$ 0.1 S/m
    带宽B 1 GHz 人体目标半径r 10 cm
    墙体厚度h 0.2 cm 目标介电常数${\varepsilon _{\mathrm{r}}}$ 55
    发射阵元和相邻接收阵元间距${d_{{\mathrm{TR}}}}$ 0.15 接收阵元间距${d_{{\mathrm{RR}}}}$ 0.3
    发射天线数量M 2 接收天线数量N 8
    墙体介电常数${\varepsilon _{\mathrm{w}}}$ 5.0 目标电导率${\sigma _{\mathrm{r}}}$ 1.05 S/m
    下载: 导出CSV

    表  2  实验环境详细参数

    Table  2.   Detailed parameters of experimental environment

    实验环境 版本
    操作系统 Windows 10专业版64位
    CPU Intel(R) Core(TM) i7-10700K CPU @ 3.80 GHz
    GPU NAVIDIA RTX 3090
    Pytorch 1.10.2
    CUDA 11.6
    下载: 导出CSV

    表  3  不同模型的SSIM, FID, PSNR值

    Table  3.   SSIM, FID and PSNR values of different models

    模型SSIMPSNRFID
    CycleGAN[28]0.6815.5032.88
    ACycleGAN0.7318.0423.50
    ADCycleGAN0.7517.7222.39
    本文域自适应模型0.8018.7818.07
    下载: 导出CSV

    表  4  不同方法目标成像准确率(%)

    Table  4.   Target imaging accuracy of different methods (%)

    方法 数据集 迁移学习 单目标准确率 双目标准确率 三目标准确率 总准确率
    1 实测数据集 × 94.72 34.90 17.24 48.95
    2 CycleGAN[28]迁移学习生成数据集 95.95 50.69 39.66 62.10
    3 本文域自适应模型迁移学习数据集 98.24 90.97 55.17 81.46
    下载: 导出CSV
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  • 收稿日期:  2024-03-29
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