Dynamic Feature-Fusion Network for Few-Shot Open-Set SAR Jamming Recognition

Abstract

The rise of cognitive electronic warfare has made synthetic aperture radar (SAR) systems increasingly vulnerable to active jamming, demanding intelligent perception and decision-making under uncertainty. This work formulates SAR jamming recognition as a cognitive sensing problem and proposes a Dynamic Feature-Fusion Network (DF2Net) tailored for Few-Shot Open-Set SAR Jamming Recognition (FSOS-JR). DF2Net employs a dual-stream attention extractor that jointly models global spatial-frequency patterns and local channel semantics, enabling robust representation learning from limited supervision. A dual-branch interaction module integrates a global-stream prototypical classifier for known-class identification with a local-stream uncertainty estimator for rejecting unknown jamming types. Furthermore, an energy-based confidence metric fuses semantic and spatial cues into a unified scoring mechanism. Extensive evaluations on both the simulated JamSet and the real-world Sen1-Jam benchmarks demonstrate that DF2Net consistently achieves state-of-the-art performance across diverse jamming conditions, highlighting its strong generalization, open-set reliability, and practical utility for real-world electromagnetic threat scenarios.