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High-resolution Imaging Method for Through-the-wall Radar Based on Transfer Learning with Simulation Samples
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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%。Abstract: This paper addresses the problem of high-resolution imaging of shadowed multiple-targets with limited labeled data, by proposing a transfer-learning-based method for through-the-wall radar imaging. First, a generative adversarial sub-network is developed to facilitate the migration of labeled simulation data to measured data, overcoming the difficulty of generating labeled data. This method incorporates an attention mechanism, adaptive residual blocks, and a multi-scale discriminator to improve the quality of image migration. It also incorporates a structural consistency loss function to minimize perceptual differences between images. Finally, the labeled data are used to train the through-the-wall radar target-imaging sub-network, achieving high-resolution imaging of multiple targets through walls. Experimental results show that the proposed method effectively reduces discrepancies between simulated and obtained images, and generates pseudo-measured images with labels. It systematically addresses issues such as side/grating ghost interference, target image defocusing, and multi-target mutual interference, significantly improving the multi-target imaging quality of the through-the-wall radar. The imaging accuracy achieved is 98.24%, 90.97% and 55.17% for single, double, and triple-target scenarios, respectively. Compared with CycleGAN, the imaging accuracy for the corresponding scenarios is improved by 2.29%, 40.28% and 15.51%, respectively.
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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 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 
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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
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