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

Abstract

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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Authors (2)

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

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Publication version: Accepted or Published Version
DOI:: Digital Object Identifier (open in new tab) 10.1109/TMTT.2021.3105677
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Keywords

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

Details

Category:

Articles

Type:

artykuły w czasopismach

Published in:

IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES no. 70, pages 264 - 274,
ISSN: 0018-9480

Language:

English

Publication year:

2022

Bibliographic description:

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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