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基于特征参数提取的智能电磁散射分析方法进展OA

Progress in intelligent electromagnetic scattering analysis methods based on the extraction of feature parameters

中文摘要英文摘要

计算电磁学(computational electromagnetics,CEM)方法在雷达目标特性优化、天线分析设计、微波器件与系统建模等电磁工程应用中处于核心地位.传统的CEM方法历经半个多世纪的发展已趋于成熟.近年来,得益于人工智能的快速发展,智能电磁计算这一新兴领域受到高度关注,旨在降低传统方法的技术实现难度,在保证结果精度的条件下大幅提高仿真速度.本文聚焦于智能电磁计算在电磁散射分析方面的应用,讨论了一类基于目标特征参数学习与提取的智能电磁计算方法.其中的特征参数为一组连接目标结构属性与目标电磁特性的参数,具有类似于系统传递函数的功能,一旦获得便可用来计算针对任何入射和观测方向的散射远场.同时,对该类方法的下一步发展进行了展望.

Computational electromagnetics(CEM)occupies the core position in the field of electromagnetic engineering applications,such as optimization of radar target characteristics,analysis and design of antennas,and modeling of microwave devices and systems.Traditional CEM methods have matured after the researches over the past half century.In recent years,benefiting from the rapid development of artificial intelligence,intelligent electromagnetic computing has drawn an increasing attention as an emerging front.It aims to lower the technical implementation difficulty of traditional CEM methods,and greatly accelerate the simulation process under the condition of ensuring result accuracy.Focusing on the applications in electromagnetic scattering analysis,this paper discusses the intelligent electromagnetic computing methods based on the learning and extraction of target feature parameters,which are a group of parameters that connect the target structural attributes to the target electromagnetic characteristics.They can possess the functions similar to the system transfer function.Once obtained,they can be utilized to calculate the scattered far fields for arbitrary incident and observation directions.A brief outlook is given to this kind of methods in the near future.

张文炜;曹嘉宁;康家齐;黄文驰;孔德铧;夏明耀

北京大学,北京 100871北京大学,北京 100871北京大学,北京 100871北京大学,北京 100871新加坡国立大学,新加坡 119077北京大学,北京 100871

数理科学

智能电磁计算电磁散射特性目标特征参数机器学习(ML)特征提取

intelligent electromagnetic computingelectromagnetic scattering characteristicstarget feature parametersmachine learningfeature extraction

《电波科学学报》 2026 (1)

42-52,11

国家自然科学基金(62231001,62171005)National Natural Science Foundation of China(62231001,62171005)

10.12265/j.cjors.2025185

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