用深度学习设计多频段吸波结构，仅凭单元尺寸就能预测吸波性能。
Design of Resistive Frequency Selective Surface based Radar Absorbing Structure-A Deep Learning Approach
- 用深度学习模型根据反射系数反推十字形单元尺寸。
- 可实现从L到Ka波段的宽频吸波，仿真验证效果优异。
- 适合快速低成本设计单单元多频吸波材料，工程落地性强。
本文提出一种基于深度学习的电阻型频率选择表面雷达吸波结构设计方法。以反射系数为输入，预测基于耶路撒冷十字形单元的几何尺寸作为输出。采用含自适应矩估计优化器的序列神经网络，实现多频段吸波器的设计。该模型可根据单元参数与厚度，在L至Ka波段范围内生成吸波结构。深度学习结果经全波仿真软件对比，匹配度极佳。所提方法可实现基于单一单元与厚度的低成本、多频段雷达吸波结构设计。
原文摘要 · Abstract (English)
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.
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