基于电阻性频率选择表面的雷达吸波结构设计——一种深度学习方法
Design of Resistive Frequency Selective Surface based Radar Absorbing Structure-A Deep Learning Approach
AI总结：
本文提出一种基于深度学习的电阻性频率选择表面雷达吸波结构设计方法，以反射系数为输入预测耶路撒冷十字单元尺寸，实现L至Ka波段多频段吸波器设计，并与全波仿真结果高度吻合，支持低成本设计。
摘要：
本文提出了一种基于深度学习的方法，用于设计采用电阻性频率选择表面的雷达吸波结构。在本设计中，反射系数被用作深度学习模型的输入，而预测输出为基于耶路撒冷十字的单元尺寸。采用带有自适应矩估计优化器的序列神经网络深度学习模型，用于设计多频段吸波器。该模型根据单元参数和厚度，用于设计从L波段到Ka波段的雷达吸波器。深度学习模型的输出进一步与全波仿真软件进行了比较，获得了极佳的一致性。所提出的模型可利用单一单元和厚度，在频带范围内低成本地设计各种雷达吸波结构。
英文摘要：
In this paper, deep learning-based approach for the design of radar absorbing structure using resistive frequency selective surface is proposed. In the present design, reflection coefficient is used as input of deep learning model and the Jerusalem cross based unit cell dimensions is predicted as outcome. Sequential neural network based deep learning model with adaptive moment estimation optimizer is used for designing multi frequency band absorbers. The model is used for designing radar absorber from L to Ka band depending on unit cell parameters and thickness. The outcome of deep learning model is further compared with full-wave simulation software and an excellent match is obtained. The proposed model can be used for the low-cost design of various radar absorbing structures using a single unit cell and thickness across the band of frequencies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
