空域电磁场快速重构的随机分布优化方法

陈方舟; 阚银辉

doi:10.12263/DZXB.20211532

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空域电磁场快速重构的随机分布优化方法

学术论文 | 更新时间：2025-12-08

- 空域电磁场快速重构的随机分布优化方法
- The Stochastic Optimization for the Prompt Reconstruction of Spatial EM Fields
- 电子学报 2023年51卷第9期页码：2313-2320
- 作者机构：
  
  南京航空航天大学，江苏南京 210016
- 作者简介：
  
  [ "陈方舟男，2019年获得美国休斯顿大学工学博士学位.现为南京航空航天大学航天学院副研究员、硕士生导师.从事计算电磁学、光学超表面、纳米电磁学研究.主持国防科技基础加强计划基金项目、“核高基”重大专项研究子课题. E-mail: fchen1990@nuaa.edu.cn" ]
  [ "阚银辉（通讯作者）男，上海交通大学获得工学博士学位.现为南京航空航天大学航天学院副教授、硕士生导师.研究方向为微纳光子器件、红外辐射调控、光电探测、量子光学超表面等.以第一作者在Adv. Mater.、ACS Photonics、Phys. Rev. Appl等权威期刊发表学术成果十余篇，申报国家发明专利多项." ]
- 基金信息：
- DOI：10.12263/DZXB.20211532
  中图分类号： TP391
- 收稿：2021-11-17，
  
  修回：2023-04-28，
  
  纸质出版：2023-09-25
- 稿件说明：
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陈方舟,阚银辉.空域电磁场快速重构的随机分布优化方法[J].电子学报,2023,51(09):2313-2320.

CHEN Fang-zhou,KAN Yin-hui.The Stochastic Optimization for the Prompt Reconstruction of Spatial EM Fields[J].ACTA ELECTRONICA SINICA,2023,51(09):2313-2320.
陈方舟,阚银辉.空域电磁场快速重构的随机分布优化方法[J].电子学报,2023,51(09):2313-2320. DOI： 10.12263/DZXB.20211532.

CHEN Fang-zhou,KAN Yin-hui.The Stochastic Optimization for the Prompt Reconstruction of Spatial EM Fields[J].ACTA ELECTRONICA SINICA,2023,51(09):2313-2320. DOI： 10.12263/DZXB.20211532.

摘要

针对压缩空间维度算法后空域电磁场重构存在冗余计算导致效率较低，且缺少合理模型支撑积分运算，提出一种高斯-双指数混合分布函数拟合频域电磁信号包络的新方法.通过确定混合分布的关键系数与主要空间变量的映射关系，快速建立针对特定空间的混合分布函数.以函数模式代替信号包络，用随机分布方法指导自适应采样点的排列.通过与多种积分方法的比较，从采样点数和不同空间变量等多个维度，验证了所提方法具有更高的准确性和有效性.此方法在频域电磁信号积分的运算中都具有运用条件，对提高3D实时仿真、建立高速反演算法都有应用前景.

Abstract

In the reconstruction of spatial electromagnetic (EM) signals based on the spatial-domain compression technique

a novel method using Gauss-Laplace mixture distribution fitting the spectral EM signal envelop is proposed

to alleviate the redundant calculation that causes low system efficiency

and patch up the absence of reliable model which can support the integration process. By determining the mapping correlation between the key coefficients and the distinctive space parameters

the mixture distribution to the specific calculation space is promptly established. Replacing the signal envelop by the distribution pattern

it adaptively rearranges the sampling points through the stochastic process. The peer comparison of various integration techniques with this novel method

from the aspects of the number of sampling and the distinctive space parameters

verifies it more accurate and efficient. The proposed method obtains a wide application condition in the computation of spectral EM signal integration

and the potential of improving 3D real-time simulation and high-speed inversion method.

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