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Reflectarray antenna direct optimization using surrogate models with several geometrical degrees of freedom per polarization

Author:
Rodriguez Prado, DanielUniovi authority; López Fernández, Jesús AlbertoUniovi authority; Arrebola Baena, ManuelUniovi authority
Subject:

reflectarray antenna optimization

surrogate model

support vector regression (SVR)

contoured-beam antenna

generalized intersection approach

Publication date:
2022-07
Descripción física:
4 p.
Abstract:

In this work, surrogate models based on support vector regression (SVR) of a multi-resonant unit cell with several degrees of freedom (DoF) per polarization are trained and used in a reflectarray antenna design and optimization. Since the unit cell has multiple sharp resonances when considering several DoF, the training process is carried out in a hyper-rectangle around a plane of stability. Results of SVR models with four geometrical DoF are shown to provide highly accurate results for the design and analysis of a very large contoured-beam reflectarray for space applications. The direct optimization layout with the surrogate models allows to improve the cross-polarization figures of merit several dB.

In this work, surrogate models based on support vector regression (SVR) of a multi-resonant unit cell with several degrees of freedom (DoF) per polarization are trained and used in a reflectarray antenna design and optimization. Since the unit cell has multiple sharp resonances when considering several DoF, the training process is carried out in a hyper-rectangle around a plane of stability. Results of SVR models with four geometrical DoF are shown to provide highly accurate results for the design and analysis of a very large contoured-beam reflectarray for space applications. The direct optimization layout with the surrogate models allows to improve the cross-polarization figures of merit several dB.

Description:

IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, NEMO 2022 (2022. Limoges, France)

URI:
http://hdl.handle.net/10651/65721
Patrocinado por:

This work was supported in part by the Ministerio de Ciencia, Innovación y Universidades under project IJC2018-035696-I; by the Ministerio de Ciencia e Innovación and the Agencia Estatal de Investigación within project ENHANCE-5G (PID2020-114172RB-C21 / AEI / 10.13039/501100011033); by Gobierno del Principado de Asturias under project AYUD/2021/51706.

Id. Proyecto:

MICIU/IJC2018-035696-I

MICINN/PID2020-114172RB-C21

AYUD/2021/51706

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