부산대학교 도서관

In many realistic scenarios, it is necessary but challenging to acquire a large number of annotated radar emitter samples for training a recognition model. This study proposes a cross-domain radar emitter recognition method with few-shot learning, which introduces model-agnostic meta-learning (MAML) to recognize radar emitter and improves it to adapt to various types of radar emitter in different domains without spending a lot of time and data to retrain the model. This method can learn an ideal initialization parameter with a few source domain samples, and then initialize with this parameter on a completely different target domain. It can obtain good generalization effect by fine-tuning with fewer samples, so as to realize cross-domain recognition of radar emitter. Simulation results show that the accuracy of the proposed method can reach more than 90% in high-noise target domains with completely different data distribution from the source domain, which shows its superiority in recognition performance and sample size compared with other radar emitter recognition methods.