Feature-Level Fusion Recognition of Space Targets With Composite Micromotion

For space targets with the same shapes and composite relationships in terms of micromotion forms (STSSCRMFs), the accuracy of deep learning approaches with single-domain data is seriously degraded. To address this issue, this article proposes a hybrid neural network based on feature-level fusion that utilizes multidomain radar information to recognize STSSCRMFs. The proposed network simultaneously processes radar cross-section (RCS) time series, high-resolution range profile (HRRP) sequences, and time-frequency (TF) spectrograms through three branches, leveraging their complementary characteristics. Additionally, an attention mechanism is incorporated into the feature-level fusion module to selectively enhance the features of important domains, thereby achieving improved space target recognition. Consequently, the temporal-spatial features of multidomain data are adaptively fused, capturing discriminative and complementary representations, which effectively enhances the accuracy of space target recognition. Experimental results demonstrate the superiority and effectiveness of the proposed method.