基于并行特征融合网络的雷达信号分类方法OA

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.