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
收藏
页码：1174 / 1178
页数：5
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