A Machine Learning Method for 2-D Scattered Far-Field Prediction Based on Wave Coefficients

被引：5

作者：

Zhang, Wen-Wei ^{[1
]}

Kong, De-Hua ^{[1
]}

He, Xiao-Yang ^{[1
]}

Xia, Ming-Yao ^{[1
]}

机构：

[1] Peking Univ, Sch Elect, Beijing 100871, Peoples R China

来源：

IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS | 2023年 / 22卷 / 05期

基金：

中国国家自然科学基金;

关键词：

Method of moments; Electromagnetic scattering; Machine learning; Surface waves; Radar cross-sections; Harmonic analysis; Electromagnetics; Electromagnetic (EM) scattering; machine learning (ML); radar cross-section (RCS); wave coefficients (WCs);

D O I：

10.1109/LAWP.2023.3235928

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this letter, a machine learning method is presented to evaluate the scattering by 2-D conducting objects. First, the scattered far field is expressed by angular harmonics with weighted wave coefficients (WCs), which are distinctive to the cross-section of the scatterer. Then, a neural network (NN) is trained to learn the WCs from a range of objects. Finally, the NN is used to extract the WCs for a given object, and the scattered far field or radar cross-section is readily computed by using the WCs. Numerical examples show that the proposed approach can be a viable choice for fast online prediction.

引用

页码：1174 / 1178

页数：5

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