Design of Resistive Frequency Selective Surface based Radar Absorbing Structure-A Deep Learning Approach
This work addresses the need for low-cost design of radar absorbing structures in electromagnetic applications, though it appears incremental as it applies existing deep learning methods to a specific domain problem.
The paper tackles the design of radar absorbing structures using resistive frequency selective surfaces by proposing a deep learning model that predicts unit cell dimensions from reflection coefficients, achieving an excellent match with full-wave simulations across L to Ka bands.
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.