雷达信号识别方法研究综述:从传统机器学习到深度学习OA

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.