基于仿真样本迁移学习的穿墙雷达高分辨成像方法  

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

作　　者：陈一凡[1] 刘剑刚 贾勇[1] 郭世盛 崔国龙 CHEN Yifan;LIU Jiangang;JIA Yong;GUO Shisheng;CUI Guolong(Chengdu University of Technology,Chengdu 610059,China;Yangtze Delta Region Institute(Quzhou),University of Electronic Science and Technology of China,Quzhou 324000,China;School of Information and Communication Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China)

机构地区：[1]成都理工大学,成都610059 [2]电子科技大学长三角研究院(衢州),衢州324000 [3]电子科技大学信息与通信工程学院,成都611731

出　　处：《雷达学报（中英文）》2024年第4期807-821,共15页Journal of Radars

基　　金：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)。

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%。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.

关 键 词：迁移学习 生成对抗网络 域自适应 穿墙雷达 高分辨成像 

分 类 号：TN958[电子电信—信号与信息处理]