吉林大学学报（信息科学版）2026，Vol.44Issue(1)：9-17,9.
基于并行特征融合网络的雷达信号分类方法
Radar Signal Classification Method Based on Parallel Feature Fusion Networks
摘要
Abstract
At present,the majority of neural network-based radar modulation signal recognition algorithms predominantly rely on a single source of information,overlooking the potential benefits that arise from the synergistic use of multi-modal information features.To tackle this limitation,a novel multi-modal parallel feature fusion model has been proposed,which leverages both one-dimensional signal sequences and two-dimensional time-frequency representations.Initially,the temporal feature extraction module incorporates a two-dimensional temporal change modeling approach to capture temporal dynamics,while the frequency domain feature extraction module employs an inverse residual structure with a band linear bottleneck layer to extract spectral features.Subsequently,the integration of two distinct attention mechanisms,along with residual connections,facilitates an effective fusion of multi-modal features,enhancing their complementary nature.Empirical evaluations conducted on DeepRadar2022 and self-built datasets demonstrate that this model provides a more comprehensive feature representation achieves higher classification accuracy and exhibits noise resilience,making it a promising solution for advanced radar signal processing applications.关键词
雷达调制信号识别/特征融合/注意力机制/时序二维变化/逆残差模块Key words
radar modulation signals recognition/feature fusion/attention mechanisms/temporal two-dimensional changes/inverse residual module分类
信息技术与安全科学引用本文复制引用
杨翼,胡远江,吴湘宁,潘志鹏,王梦雪..基于并行特征融合网络的雷达信号分类方法[J].吉林大学学报（信息科学版）,2026,44(1):9-17,9.基金项目
国家自然科学基金资助项目(U21A2013) （U21A2013）
湖北省自然科学基金资助项目(2021CFB506) （2021CFB506）
