Structure-Informed Deep Filtering for Radar HRRP ISRJ Mitigation

Abstract

Interrupted sampling repeater jamming (ISRJ) severely degrades high-resolution range profile (HRRP)-based radar target recognition by generating deceptive scatterers. However, existing learning-based ISRJ suppression methods tend to ignore the structural priors inherent in radar echoes, limiting their robustness and generalization. To address this issue, this letter proposes a structure-informed ISRJ filtering network, termed ISRJ-FNet, for robust HRRP recovery. The proposed method embeds two explicit priors into a deep filtering framework: the symmetric range distribution of ISRJ and the sparse range-localized structure of target echoes. A symmetric dual-branch architecture is further introduced to improve mask consistency under target perturbations. Experiments on measured and simulated data show that ISRJ-FNet achieves better performance than representative deep suppression baselines. The results demonstrate the effectiveness and robustness of structure-informed deep filtering for radar anti-jamming applications.