Few-Shot Open-Set Specific Emitter Identification: A Contrastive Learning Approach With False Negative Suppression

Deep learning has shown good performance in specific emitter identification (SEI) with sufficient labeled datasets. However, in practical deployments, labeled samples are limited, while unknown open-set emitters affect identification accuracy. Hence, we propose a few-shot open-set SEI approach based on contrastive learning approach with false negative suppression. Specifically, contrastive learning is designed to pre-train the feature extractor using sufficient unlabeled auxiliary samples. This framework solves the problem of false negatives in SEI, which otherwise degrades representation learning. Subsequently, the feature extractor is fine-tuned using a small number of labeled samples on the target domain. Additionally, adaptive threshold is used for open-set recognition. ADS-B and Wi-Fi datasets are used to evaluate the proposed approach. Compared to other state-of-the-art approaches, our proposed approach improves the few-shot SEI performance under both open-set and close-set conditions.