基于电磁仿真和机器学习的快速目标成像模型  

A fast target imaging model based on electromagnetic simulation and machine learning

作　　者：黄猛 李少猛 王玉菊 王青山 代维凯 张思维 李典 HUANG Meng;LI Shaomeng;WANG Yuju;WANG Qingshan;DAI Weikai;ZHANG Siwei;LI Dian(Chinese People’s Liberation Army Unit 91977,Beijing 102249,China;China State Shipbuilding Corporation 714 Research Institute,Beijing 100101,China)

机构地区：[1]中国人民解放军91977部队,北京102249 [2]中国船舶集团有限公司第七一四研究所,北京100101

出　　处：《电波科学学报》2025年第1期58-62,共5页Chinese Journal of Radio Science

摘　　要：随着雷达技术的快速发展,传统的基于电磁(electromagnetism,EM)散射计算的逆合成孔径雷达(inverse synthetic aperture radar,ISAR)图像仿真方法往往面临时间成本高的挑战,难以实时生成目标的高分辨率ISAR图像样本。针对复杂目标图像样本数据集构建效率低的问题,本文提出了一种基于机器学习的ISAR图像快速预测模型。该模型利用少量的ISAR回波数据作为复杂目标的EM计算输入,通过数据增强技术提高数据集的多样性,进一步采用动态加权集成技术,将线性回归、支持向量机以及随机森林等三种常见的回归模型进行结合。所提出的集成模型可以快速预测ISAR回波数据,减少EM模拟计算的次数,并显著提高样本生成的效率。实验结果表明:本文模型仅使用较少回波数据就能准确预测生成图像所需的全部数据,总体效率提高约80%;随着目标复杂性和分辨率的增加,使用仿真方法所需时间将显著增加,本文模型优势更明显。With the rapid advancement of radar technology,traditional inverse synthetic aperture radar(ISAR)image simulation methods based on electromagnetic(EM)scattering calculations often face the challenge of high time costs,making it difficult to generate high-resolution ISAR image samples in real time.To address the issue of low efficiency in constructing image sample datasets for complex targets,a machine learning-based model for fast ISAR image prediction is proposed.This model uses a small amount of ISAR echo data as the input for EM calculations of complex targets.Data augmentation techniques are employed to increase dataset diversity,and a dynamic weighted ensemble method is applied to integrate three commonly used regression models:linear regression,support vector machine(SVM),and random forest.The proposed ensemble model can rapidly predict ISAR echo data,reducing the number of EM simulations required and significantly improving the efficiency of sample generation.Experimental results demonstrate that the model can accurately predict all the data needed to generate images using only a small amount of echo data,achieving an overall efficiency improvement of approximately 80%.As the complexity and resolution of the target increase,the time required for simulation methods will increase significantly.At this point,our proposed model will demonstrate greater advantages.

关 键 词：逆合成孔径雷达(ISAR)图像 电磁(EM)散射计算 机器学习 集成方法 数据增强 

分 类 号：O44[理学—电磁学]