基于随机调制超表面的差分关联成像方法研究

年毅恒 周宁宁 朱士涛 张安学

年毅恒, 周宁宁, 朱士涛, 等. 基于随机调制超表面的差分关联成像方法研究[J]. 雷达学报, 2021, 10(2): 296–303. doi: 10.12000/JR20136
引用本文: 年毅恒, 周宁宁, 朱士涛, 等. 基于随机调制超表面的差分关联成像方法研究[J]. 雷达学报, 2021, 10(2): 296–303. doi: 10.12000/JR20136
NIAN Yiheng, ZHOU Ningning, ZHU Shitao, et al. Differential coincidence imaging based on randomly modulated metamaterial surface[J]. Journal of Radars, 2021, 10(2): 296–303. doi: 10.12000/JR20136
Citation: NIAN Yiheng, ZHOU Ningning, ZHU Shitao, et al. Differential coincidence imaging based on randomly modulated metamaterial surface[J]. Journal of Radars, 2021, 10(2): 296–303. doi: 10.12000/JR20136

基于随机调制超表面的差分关联成像方法研究

doi: 10.12000/JR20136
基金项目: 国家自然科学基金(62071371),超高速电路设计与电磁兼容教育部重点实验室资助(LHJJ/2020-04),雷达信号处理国防科技重点实验室资助
详细信息
    作者简介:

    年毅恒(1995–),男,安徽蚌埠人,硕士,现为西安交通大学信息与通信工程学院博士生,主要研究方向为微波关联成像、雷达信号处理。E-mail: nyhxjtu2019@stu.xjtu.edu.cn

    周宁宁(1996–),女,河南周口人,学士,现为西安交通大学信息与通信工程学硕士生,主要研究方向为基于OAM的关联成像算法、传输超表面产生OAM。E-mail: zhouning96@stu.xjtu.edu.cn

    朱士涛(1980–),男,河北沧州人,博士,现为西安交通大学信息与通信工程学院副研究员,硕士生导师,主要研究方向为新型雷达信号处理方法、人工智能成像算法、微波关联成像、超材料孔径天线及微波量子雷达。E-mail: shitaozhu@xjtu.edu.cn

    张安学(1972–),男,河南安阳人,博士,现为西安交通大学电磁与信息技术研究所所长,教授,博士生导师,主要研究方向为新型天线与分集技术、移动通信微波射频技术、智能雷达信号处理、多天线通信系统与阵列信号处理、微波测试理论与系统设计等。E-mail: anxuezhang@mail.xjtu.edu.cn

    通讯作者:

    朱士涛 shitaozhu@xjtu.edu.cn

  • 责任主编:李廉林 Corresponding Editor: LI Lianlin
  • 中图分类号: TN95

Differential Coincidence Imaging Based on a Randomly Modulated Metamaterial Surface

Funds: The National Natural Science Foundation of China (62071371), The Key Laboratory of High-Speed Circuit Design and EMC Ministry of Education (LHJJ/2020-04), The National Key Lab of Radar Signal Processing
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  • 摘要: 基于超表面的关联成像系统解决了关联成像系统探测效率低的问题,但其探测模式数量不足导致了其有效成像点数受限。针对这个问题,该文以参考辐射场空间分布1阶统计特征为基础,建立了基于随机调制超表面的关联成像信号模型,分析了成像误差,并与差分关联成像(DCI)方法相结合,给出了具有鲁棒性的基于超表面的关联成像方法,该方法利用不同模式的差分形成了新的探测模式,降低了相关函数的副瓣干扰,从而提升了成像质量。同时,对一种特殊的差分关联成像方法—梯度关联成像(GCI)方法的成像分辨率进行了分析,该方法通过对超表面单元的特殊设计,可以在不获取图像的情况下,直接在成像过程中提取出目标方位向的边缘信息,可以有效提升关联成像系统对目标边缘的提取能力。最后,通过仿真实验验证了该文理论分析的正确性。

     

  • 图  1  线性阵列信号模型示意图

    Figure  1.  Linear array signal model

    图  2  差分关联成像相关函数

    Figure  2.  The correlation function in differential coincidence imaging

    图  3  传统与差分关联成像不同辐射样本的相关系数

    Figure  3.  The correlation coefficient of detection modes in coincidence imaging and Differential Coincidence Imaging (DCI)

    图  4  传统与梯度关联成像的对比

    Figure  4.  The comparion between coincidence imaging and Gradient Coincidence Imaging (GCI)

    图  5  传统关联成像与梯度关联成像对比

    Figure  5.  The comparion of imaging between coincidence imaging and Gradient Coincidence Imaging (GCI)

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出版历程
  • 收稿日期:  2020-11-01
  • 修回日期:  2021-01-11
  • 网络出版日期:  2021-04-28

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