Supervised-learning-based development of multi-bit RCS-reduced coding metasurfaces

Muhammad Abdullah; Sławomir Kozieł

doi:10.1109/tmtt.2021.3105677

Supervised-learning-based development of multi-bit RCS-reduced coding metasurfaces

Abstrakt

Coding metasurfaces have been introduced as efficient tools allowing meticulous control over the electromagnetic (EM) scattering. One of their relevant application areas is radar cross section (RCS) reduction, which principally relies on the diffusion of impinging EM waves. Despite its significance, careful control of the scattering properties poses a serious challenge at the level of practical realization. This article is concerned with (global) design optimization of coding metasurfaces featuring broadband RCS reduction. We adopt a two-stage optimization procedure involving data-driven supervised-learning, sequential-search strategy, and direct EM-based design closure of the entire metasurface oriented toward maximizing the RCS reduction bandwidth. Our framework is then used to develop a two-bit coding metasurface. To handle the combinatorial explosion at the concurrent meta-atom optimization stage, a sequential-search strategy has been developed that enables global search capability at low computational cost. Finally, EM-based optimization is executed to maximize RCS reduction bandwidth at the level of entire metasurface. The properties of the coding metasurface are demonstrated using monostatic and bistatic RCS performance. The 10-dB RCS reduction can be obtained in the frequency range of 14.8 GHz to 37.2 GHz, in a monostatic configuration. Also, 15-dB RCS reduction can be maintained in the frequency range of 16.7 GHz to 37 GHz. Simulations are validated using physical measurements of the fabricated prototypes. Finally, the performance of the structure is benchmarked against recently reported designs.

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Autorzy (2)

Muhammad Abdullah
- Reykjavik University
Sławomir Kozieł prof. dr inż.

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Wersja publikacji: Accepted albo Published Version
DOI:: Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1109/TMTT.2021.3105677
Licencja: Copyright (2021 IEEE)

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Słowa kluczowe

CODING METASURFACE, SUPERVISED LEARNING, BEAM MANIPULATION, DIFFUSION, RADAR CROSS SECTION (RCS)

Informacje szczegółowe

Kategoria:

Publikacja w czasopiśmie

Typ:

artykuły w czasopismach

Opublikowano w:

IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES nr 70, strony 264 - 274,
ISSN: 0018-9480

Język:

angielski

Rok wydania:

2022

Opis bibliograficzny:

Abdullah M., Kozieł S.: Supervised-learning-based development of multi-bit RCS-reduced coding metasurfaces// IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES -Vol. 70,iss. 1 (2022), s.264-274

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