Open-Set Recognition of Communication Jamming Using Raw I/Q Data With Domain Adaptation

Effective recognition of jamming in a communication system is essential to maintain the integrity of the electromagnetic spectrum space. In this paper, a novel feature-enhanced open-set jamming pattern recognition method (FOSR) is proposed. First, an in-phase and quadrature (I/Q) data feature enhancement module is designed based on a complex-valued autoencoder to capture the interaction features between the I and Q channels. Then, a jamming feature extraction module is designed to extract jamming characteristics for known patterns by integrating the raw I/Q data with their interaction features. Subsequently, an adaptive threshold open-set classification module is proposed to recognize both known and unknown patterns. Finally, to address the domain shift problem, we extend FOSR with a domain adaptation (DA) module based on distribution alignment and classifier calibration, referred to as FOSR-DA. Simulation results show that the proposed method achieves superior recognition accuracy and exhibits strong robustness when dealing with the domain shift problem.