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

共 50 条

[21] Comment on "Multiple scattering: The key to unravel the subwavelength world from the far-field pattern of a scattered wave"
de Rosny, Julien
Prada, Claire
PHYSICAL REVIEW E, 2007, 75 (04):
[22] Prediction of Far Field from Near Field using Machine Learning
Ikeda H.
Journal of Japan Institute of Electronics Packaging, 2024, 27 (03) : 220 - 225
[23] Hybrid multilevel plane wave based near-field far-field transformation utilising combined near- and far-field translations
Schmidt, C. H.
Eibert, T. E.
ADVANCES IN RADIO SCIENCE, 2009, 7 : 17 - 22
[24] Machine Learning for 2-D Scattering Analysis using Method of Moments
Olcan, Dragan
Petrovic, Jovana
Kolundzija, Branko
2020 IEEE INTERNATIONAL SYMPOSIUM ON ANTENNAS AND PROPAGATION AND NORTH AMERICAN RADIO SCIENCE MEETING, 2020, : 1985 - 1986
[25] Vectorial far-field method for subwavelength grating based VCSELs
Qi, Yuxuan
Li, Wei
Lv, Jiagang
Pan, Zhipeng
Nie, Yuwei
Liu, Suping
Ma, Xiaoyu
PHOTONICS AND NANOSTRUCTURES-FUNDAMENTALS AND APPLICATIONS, 2023, 55
[26] Coherent beam combination far-field measuring method based on amplitude modulation and deep learning
刘人奇
彭纯
梁晓燕
李儒新
ChineseOpticsLetters, 2020, 18 (04) : 25 - 29
[27] Coherent beam combination far-field measuring method based on amplitude modulation and deep learning
Liu, Renqi
Peng, Chun
Liang, Xiaoyan
Li, Ruxin
CHINESE OPTICS LETTERS, 2020, 18 (04)
[28] Subdivision Technique in Near-Field Far-Field Transformation for 2-D Radar Cross Section Measurement for Large Objects
Omi, Shuntaro
Uno, Toru
Arima, Takuji
Fujii, Takao
2017 IEEE CONFERENCE ON ANTENNA MEASUREMENTS & APPLICATIONS (CAMA), 2017, : 348 - 350
[29] A sound extrapolation method for aeroacoustics far-field prediction in presence of vortical waves
Zhong, Siyang
Zhang, Xin
JOURNAL OF FLUID MECHANICS, 2017, 820 : 424 - 450
[30] Effective Far-Field Acoustic Prediction Method and its Computational Aeroacoustics Applications
Yu, Chao
Zhou, Zhengfang
Zhuang, Mei
Li, Xiaodong
Thiele, Frank
AIAA JOURNAL, 2009, 47 (02) : 410 - 417

← 1 2 3 4 5 →