基于集成学习的雷达自动目标识别综述  

Review of radar automatic target recognition based on ensemble learning

作　　者：洪梓榕 包广清 HONG Zirong;BAO Guangqing(College of Electrical Engineering and Information Engineering,Lanzhou University of Technology,Lanzhou Gansu 730050,China;School of Electrical Engineering and Information,Southwest Petroleum University,Chengdu Sichuan 610500,China)

机构地区：[1]兰州理工大学电气工程与信息工程学院,兰州730050 [2]西南石油大学电气信息学院,成都610500

出　　处：《计算机应用》2025年第2期371-382,共12页journal of Computer Applications

基　　金：甘肃省高等学校创新基金资助项目(2023A-201)。

摘　　要：雷达自动目标识别(RATR)在军事和民用领域中都有广泛的应用。由于集成学习通过集成已有的机器学习模型改善模型分类性能,具有较好的鲁棒性,因此被越来越多地应用于雷达目标检测与识别领域。系统梳理和提炼现有相关文献对集成学习在RATR中的研究进展。首先,介绍集成学习的概念、框架与发展历程,将集成学习与传统机器学习、深度学习方法对比,并总结集成学习理论和常见集成学习方法的优势、不足及研究的主要聚焦点;其次,简述RATR的概念;接着,重点阐述集成学习在不同雷达图像分类特征中的应用,详细讨论基于合成孔径雷达(SAR)和高分辨距离像(HRRP)的目标检测与识别方法,并总结这些方法的研究进展和应用成效;最后,讨论RATR以及集成学习所面临的挑战,并对集成学习在雷达目标识别领域的应用进行展望。Radar Automatic Target Recognition(RATR) has widespread applications in both domains of military and civilian.Due to the robustness caused by that ensemble learning improves model classification performance by integrating the existing machine learning models,ensemble learning has been applied in the field of radar target detection and recognition increasingly.The research progress of ensemble learning in RATR was discussed in detail through systematic sorting and refining the existing relevant literature.Firstly,the concept,framework,and development process of ensemble learning were introduced,ensemble learning was compared with traditional machine learning and deep learning methods,and the advantages,limitations,and main focuses of research of ensemble learning theory and common ensemble learning methods were summarized.Secondly,the concept of RATR was described briefly.Thirdly,the applications of ensemble learning in different radar image classification features were focused on,with a detailed discussion on target detection and recognition methods based on Synthetic Aperture Radar(SAR) and High-Resolution Range Profile(HRRP),and the research progress and application effect of these methods were summed up.Finally,the challenges faced by RATR and ensemble learning were discussed,and the applications of ensemble learning in the field of radar target recognition were prospected.

关 键 词：目标检测与识别 集成学习 合成孔径雷达 高分辨距离像 传统机器学习 深度学习 

分 类 号：TN953[电子电信—信号与信息处理]