MFFN-FCSA: Multi-Modal Feature Fusion Networks with Fully Connected Self-Attention for Radar Space Target Recognition

Radar space target recognition is faced with inherent challenges due to complex electromagnetic scattering properties and limited training samples. Conventional single-modality approaches cannot fully characterize targets due to information incompleteness, and existing multi-modal fusion methods often neglect deep exploration of cross-modal feature correlations. To address this issue, this paper presents a novel multi-modal feature fusion network with fully connected self-attention (MFFN-FCSA) for robust radar space target recognition. The proposed framework innovatively integrates multi-modal radar data, including high-resolution range profiles (HRRPs) and inverse synthetic aperture radar (ISAR) images, to exploit the complementary characteristics comprehensively. Our MFFN-FCSA consists of three modules: the parallel convolutional branches for modality-specific feature extraction of HRRPs and ISAR images, an FCSA-based fusion module for cross-modal feature fusion, and a classification head. Specially, the designed FCSA fusion module simultaneously learns spatial and channel-wise dependencies via a fully connected self-attention mechanism, which enables learning dynamic weights of discriminative features across modalities. Furthermore, our end-to-end MFFN-FCSA model incorporates a composite loss function that combines a focal cross-entropy loss to address class imbalance and a triplet margin loss for enhanced metric learning. Experimental results based on a space target dataset with 10 categories show the high recognition accuracy of our model compared to related single-modality and existing fusion approaches, particularly showing promising generalization capabilities on few-shot and polarization variation scenarios.