Physical scattering model of electrically large metasurface-coated targets using surface susceptibility model

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Authors: Wuxia Miao, Lamei Zhang, Bin Zou, Di Zhuang
Cited by: 0
References: 63

Abstract

Abstract The physical scattering analysis of electrically large targets coated with metasurfaces (MSs) has been emerged as a focal research topic in electromagnetic applications. Conventional methods face challenges, such as high computational cost and inefficiency in acquiring reflection coefficients for MSs. In this work, a physical electromagnetic scattering model for MSs (ESM-MSs) coated on electrically large targets is proposed for the first time, which exhibits great stability under broadband and wide-angle incidence. By incorporating the surface susceptibility model into the high-frequency electromagnetic method, the MS reflection operator is embedded in deviation of scattered electric field. ESM-MS effectively addresses the lack of efficient scattering analysis models for MSs applied to electrically large targets and the complexity of acquiring diverse scattering data. The effectiveness of ESM-MS is validated by two-dimensional (2D), three-dimensional (3D), electrically large targets, and practical measurements, the results of which demonstrate high accuracy in radar cross section for all structures. For 2D targets at 45° oblique incidence, the mean error is below 0.97 dBm 2 and with a consistency of 0.99. For 3D targets and car, the deviations exhibit within 2.7 dBm 2 compared to FEKO under key reflection angles. The proposed efficient electromagnetic model provides an effective analytical strategy for MS coated on electrically large targets and holds widespread prospects for modern electromagnetic MS design.

Fields of Study & Subjects

Advanced Antenna and Metasurface Technologies Electromagnetic Scattering and Analysis Metamaterials and Metasurfaces Applications

Publication details

Journal / source
Journal of Physics D Applied Physics
Volume
59
Issue
9
Pages
095005–095005
Publication date
11 February 2026
Language
EN