Specific emitter identification unaffected by time through adversarial domain adaptation and continual learning

Timely identifying the emitter of target signals is crucial for communication security in complex electromagnetic environments. Specific emitter identification (SEI) is a technique to identify emitters using the hardware fingerprints of emitters. In this paper, the impact of changes in hardware fingerprints over time on SEI is solved, which significantly deteriorates identification performance. This issue is addressed from two aspects: one is mitigating the impact of these changes, and the other is tracking and adapting to them. For the aspect of mitigating impact, an alternating adversarial domain adaptation (AADA) method is proposed to eliminate the time-varying component in hardware fingerprints. Subsequently, a feature map calculation method using weighted Euclidean distance is designed, preserving the main parameters of feature maps for each emitter. For the aspect of tracking and adapting to changes, a continual learning method was designed based on feature maps of each emitter. This approach incorporates the selective annotation of unlabeled new data with an iterative optimization training process. To validate the effectiveness of the proposed method, we independently collected comprehensive time-variant datasets as well as simpler datasets with varying receivers and environments. The proposed method was tested on these datasets and compared with existing conventional and advanced methods. The experimental results indicate that the proposed SEI method exhibits superior recognition performance. Compared to existing methods, it achieved an average recognition accuracy improvement of over 8% on the time-variant dataset, and demonstrated enhanced robustness against these three types of variations.