Wideband Monostatic RCS Prediction of Complex Objects using Support Vector Regression and Grey-wolf Optimizer

Authors

  • Zhourui Zhang School of Integrated Circuits and Electronics Beijing Institute of Technology, Beijing, 100081, China
  • Pengyuan Wang School of Integrated Circuits and Electronics Beijing Institute of Technology, Beijing, 100081, China
  • Mang He School of Integrated Circuits and Electronics Beijing Institute of Technology, Beijing, 100081, China

DOI:

https://doi.org/10.13052/2023.ACES.J.380808

Keywords:

Radar cross-section, complex objects, machine learning, support vector regression, grey wolf optimizer

Abstract

This paper presents a method based on the support vector regression (SVR) model and grey wolf optimizer (GWO) algorithm to efficiently predict the monostatic radar cross-section (mono-RCS) of complex objects over a wide angular range and frequency band. Using only a small-size of the mono-RCS data as the training set to construct the SVR model, the proposed method can predict accurate mono-RCS of complex objects under arbitrary incident angle over the entire three-dimensional space. In addition, the wideband prediction capability of the method is significantly enhanced by incorporating the meta-heuristic algorithm GWO. Numerical experiments verify the efficiency and accuracy of the proposed SVR-GWO model over a wide frequency band.

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Author Biographies

Zhourui Zhang, School of Integrated Circuits and Electronics Beijing Institute of Technology, Beijing, 100081, China

Zhourui Zhang received the B.S. degree in electrical engineering from Beijing Institute of Technology, Beijing, China, in July 2018, and the M.S. degree in electrical engineering from University of California, San Diego, US, in March 2021. He is currently pursuing the Ph.D. degree in electronic science and technology at the Beijing Institute of Technology, Beijing, China. His current research interest is yield and sensitivity analysis of antenna-radome systems.

Pengyuan Wang, School of Integrated Circuits and Electronics Beijing Institute of Technology, Beijing, 100081, China

Pengyuan Wang received the B.S. degree in communication engineering from North China Electric Power University, Baoding, China, in 2019. She is currently pursuing the Ph.D. degree in electromagnetics and microwave technology at the Beijing Institute of Technology, Beijing. Her current research interests include computational electromagnetics and parallel computation.

Mang He, School of Integrated Circuits and Electronics Beijing Institute of Technology, Beijing, 100081, China

Mang He (Senior Member, IEEE) received the B.S. and Ph.D. degrees from the Department of Electrical Engineering, Beijing Institute of Technology, Beijing, China, in 1998 and 2003, respectively. From 2003 to 2004, he was a research associate with the Department of Electronic Engineering, City University of Hong Kong, Hong Kong. From 2008 to 2009, he was a post-doctoral research fellow with the Department of Electrical and Communication Engineering, Tohoku University, Sendai, Japan. He is currently a professor at the Beijing Institute of Technology. His current research interests include computational electromagnetics and its applications, antenna theory and design, radome, and frequency-selective surface design.

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Published

2023-08-31

How to Cite

[1]
Z. . Zhang, P. . Wang, and M. . He, “Wideband Monostatic RCS Prediction of Complex Objects using Support Vector Regression and Grey-wolf Optimizer”, ACES Journal, vol. 38, no. 08, pp. 609–615, Aug. 2023.

Issue

2023: Vol 38 Iss 8

Section

General Submission

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