Design of Resistive Frequency Selective Surface based Radar Absorbing Structure-A Deep Learning Approach
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916631854186496 |
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| author | Sutrakar, Vijay Kumar Morge, Nikhil PK, Anjana PV, Abhilash |
| author_facet | Sutrakar, Vijay Kumar Morge, Nikhil PK, Anjana PV, Abhilash |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_19151 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Design of Resistive Frequency Selective Surface based Radar Absorbing Structure-A Deep Learning Approach Sutrakar, Vijay Kumar Morge, Nikhil PK, Anjana PV, Abhilash Machine Learning Materials Science Applied Physics 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. |
| title | Design of Resistive Frequency Selective Surface based Radar Absorbing Structure-A Deep Learning Approach |
| topic | Machine Learning Materials Science Applied Physics |
| url | https://arxiv.org/abs/2502.19151 |