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Design of monopole antennas based on progressive Gaussian process

Published online by Cambridge University Press:  07 March 2022

Xie Zheng Open the ORCID record for Xie Zheng [Opens in a new window] ,
Fei Meng ,
Yubo Tian  and
Xinyu Zhang
Xie Zheng
Affiliation:
School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang 212100, Jiangsu, China
Fei Meng
Affiliation:
School of Information and Communication Engineering, Guangzhou Maritime University, Guangzhou 510725, Guangdong, China
Yubo Tian*
Affiliation:
School of Information and Communication Engineering, Guangzhou Maritime University, Guangzhou 510725, Guangdong, China
Xinyu Zhang
Affiliation:
School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang 212100, Jiangsu, China
*
Author for correspondence: Yubo Tian, E-mail: tianyubo@just.edu.cn
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Abstract

Electromagnetic simulation software has become an important tool for antenna design. However, high-fidelity simulation of wideband or ultra-wideband antennas is very expensive. Therefore, antenna optimization design by using an electromagnetic solver may be limited due to its high computational cost. This problem can be alleviated by the utilization of fast and accurate surrogate models. Unfortunately, conventional surrogate models for antenna design are usually prohibitive because training data acquisition is time-consuming. In order to solve the problem, a modeling method named progressive Gaussian process (PGP) is proposed in this study. Specially, when a Gaussian process (GP) is trained, test sample with the largest predictive variance is inputted into an electromagnetic solver to simulate its results. After that, the test sample is added to the training set to train the GP progressively. The process can incrementally increase some important trusted training data and improve the model generalization performance. Based on the proposed PGP, two monopole antennas are optimized. The optimization results show effectiveness and efficiency of the method.

Keywords

Electromagnetic simulationGaussian processsurrogate model
Type
Antenna Design, Modeling and Measurements
Information
International Journal of Microwave and Wireless Technologies , Volume 15 , Issue 2 , March 2023 , pp. 255 - 262
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press in association with the European Microwave Association

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Footnotes

*

Xie Zheng and Fei Meng are co-first authors.

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