2025/01/23 by Yangbo Zhou, Zhou, Yangbo, Sen Liu +11
Engineering · #Advanced SAR Imaging Techniques #FOS: Electrical engineering #Optical Systems and Laser Technology #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2501.13541
openalex publication_date 2025/01/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recent advances in radar automatic target recognition (RATR) techniques utilizing deep neural networks have demonstrated remarkable performance, largely due to their robust generalization capabilities. To address the challenge for applications with polarimetric HRRP sequences, a dual-polarization feature fusion network (DPFFN) is proposed along with a novel two-stage feature fusion strategy. Moreover, a specific fusion loss function is developed, which enables the adaptive generation of comprehensive multi-modal representations from polarimetric HRRP sequences. Experimental results demonstrate that the proposed network significantly improves performance in radar target recognition tasks, thus validating its effectiveness. The PyTorch implementation of our proposed DPFFN is available at https://github.com/xmpan/DPFFN.