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Differential Coincidence Imaging Based on a Randomly Modulated Metamaterial Surface
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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。Abstract: The coincidence imaging system based on a metamaterial surface solves the problem of low detection efficiency. Nevertheless, the number of effective imaging points is limited owing to the lack of the detection mode. To solve this problem, based on the first-order statistical characteristics of a reference-radiation field, a correlation-imaging signal model based on a randomly-modulated metamaterial surface is established, and the imaging error is analyzed. This study presents a robust coincidence imaging called Differential Coincidence Imaging (DCI), which uses the differential of different modes to form a new detection mode. The DCI analysis proves that it can improve the imaging quality. At the same time, the resolution of a special DCI method called the Gradient Coincidence Imaging (GCI) method is analyzed, which effectively improves the ability of extracting a target edge. With the special design of a metasurface unit, the edge information of a target can be extracted directly in the imaging process without obtaining an image. Finally, the proposed methods are validated through simulation experiments.
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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)
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