基于生成模型的圆极化Fabry-Perot腔传感天线设计
Circularly Polarized Fabry-Perot Cavity Sensing Antenna Design using Generative Model
摘要：
我们考虑的问题，设计一个圆极化法布里-珀罗腔（FPC）天线的S波段传感应用，如卫星导航和通信。FPC部分反射面（PRS）单胞周边粗糙度的空间分布是FPC设计优化的重要依据。然而，使用全波求解器对每个候选设计进行评估在计算上是昂贵的。为此，我们提出了一个深度生成对抗网络（GAN），用于实现一个代理模型，该模型是用天线设计的输入-输出对及其相应的模式进行训练的。使用GAN框架，我们快速评估大量候选设计的特性，并选择轴比为0.4 dB、增益为7.5 dB、带宽为269 MHz的天线设计。
英文摘要：
We consider the problem of designing a circularly polarized Fabry-P'erot cavity (FPC) antenna for S-band sensing applications such as satellite navigation and communication. The spatial distribution of peripheral roughness of the unit cell of FPC's partially reflecting surface (PRS) serves as an important design optimization criterion. However, the evaluation of each candidate design using a full-wave solver is computationally expensive. To this end, we propose a deep generative adversarial network (GAN) for realizing a surrogate model that is trained with input-output pairs of antenna designs and their corresponding patterns. Using the GAN framework, we quickly evaluate the characteristics of a large volume of candidate designs and choose the antenna design with an axial ratio of 0.4 dB, a gain of 7.5 dB, and a bandwidth of 269 MHz.
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