无线电工程2026，Vol.56Issue(3)：470-488,19.DOI:10.3969/j.issn.1003-3106.2026.03.010
雷达信号识别方法研究综述:从传统机器学习到深度学习
Review of Research on Radar Signal Recognition Methods:From Traditional Machine Learning to Deep Learning
摘要
Abstract
Radar signal recognition is crucial for target detection,classification,and tracking in complex electromagnetic environments.As target types and signal characteristics continuously evolve,improving recognition accuracy has become a significant research challenge.To address the current limitations in recognition accuracy,this paper reviews the development of radar signal recognition methods from traditional machine learning to deep learning approaches.It analyzes traditional algorithms such as Support Vector Machine(SVM)and Random Forest(RF),highlighting their dependency on manually designed features,which makes them difficult to handle dynamic signal variations and low Signal to Noise Ratio(SNR)conditions.The paper emphasizes deep learning models,including Convolutional Neural Network(CNN),Recurrent Neural Network(RNN),and Transformer,illustrating their advantages in automatic feature extraction and nonlinear modeling,significantly improving recognition accuracy.However,these methods face challenges such as data scarcity and high computational resource demands.The paper suggests that future research should focu on advancing multi-modal feature fusion,model lightweight design,and enhancing model interpretability,aiming for breakthroughs in these areas to further improve recognition accuracy and robustness,and expand application scenarios.This review aims to provide methodological guidance and technical reference for subsequent research.关键词
复杂电磁环境/雷达信号识别/特征提取/传统机器学习/深度学习Key words
complex electromagnetic environment/radar signal recognition/feature extraction/conventional machine learning/deep learning分类
信息技术与安全科学引用本文复制引用
彭棋,刘雄章,杨雨舟,刘文杰..雷达信号识别方法研究综述:从传统机器学习到深度学习[J].无线电工程,2026,56(3):470-488,19.基金项目
国家自然科学基金(52202106) （52202106）
四川省自然科学基金(2025ZNSFSC1386) （2025ZNSFSC1386）
中国国防科技大学先进陶瓷纤维与复合材料科学技术实验室开放基金(WDZC20255290506) National Natural Science Foundation of China(52202106) （WDZC20255290506）
Sichuan Provincial Natural Science Foundation of China(2025ZNSFSC1386) （2025ZNSFSC1386）
Open Fund of the Science and Technology on Advanced Ceramic Fibers and Composites Laboratory,National University of Defense Technology,China(WDZC20255290506) （WDZC20255290506）
