Generative-contrastive learning for open set radar emitter identification

In traditional radar emitter identification (REI) tasks, both the training and testing samples share the same distribution, and the model is trained solely to recognize known targets. However, in non-cooperative electromagnetic environments, unknown classes are often absent from the training data, which may be incorrectly classified as known classes. To address this issue, we propose an innovative Generative-contrastive Learning method for Open Set REI (GLOSE) from the perspective of feature space optimization. We first introduce a conditional generative model derived from diffusion to generate stable interpolated samples within the feature space, which are defined as an additional class to compress the coverage of known classes, thereby enhancing the capability to handle unknown space. Subsequently, we employ contrastive learning with an adaptive contrastive loss to further optimize the discriminative power of the feature space, which applies varying levels of intra-class similarity for different types of samples. Extensive experiments are conducted on a simulated radar emitter dataset based on intra-pulse unintentional modulation and a real-world automatic dependent surveillance-broadcast (ADS-B) dataset. The results demonstrate that the proposed method significantly improves the detection capability of unknown class samples while maintaining high classification accuracy for known classes.