A Dual-Polarization Feature Fusion Network for Radar Automatic Target Recognition Based on HRRP Sequences

Recent advances in radar automatic target recognition techniques utilizing deep neural networks have demonstrated remarkable performance, largely due to their robust generalization capabilities. To address the challenge for applications with polarimetric high-resolution range profile (HRRP) sequences, a dual-polarization feature fusion network (DPFFN) is proposed along with a two-stage feature fusion strategy. Moreover, a specific fusion loss function is developed, which enables the adaptive generation of comprehensive multimodal representations from polarimetric HRRP sequences. Experimental results demonstrate that the proposed network significantly improves performance in radar target recognition tasks, thus validating its effectiveness.