Design of Resistive Frequency Selective Surface based Radar Absorbing Structure-A Deep Learning Approach

TL;DR

This paper presents a deep learning approach to design radar absorbing structures using resistive frequency selective surfaces, enabling low-cost, multi-band radar absorber design from L to Ka band with high accuracy.

Contribution

It introduces a neural network model that predicts unit cell dimensions for radar absorbers based on reflection coefficients, streamlining the design process across multiple frequency bands.

Findings

Deep learning model accurately predicts unit cell parameters.

Model matches well with full-wave simulations.

Enables cost-effective, multi-band radar absorber design.

Abstract

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.