SSME: A Semi-Supervised Specific Emitter Identification Method With Manifold Enhancement

The proliferation of Internet of Things (IoT) devices generates substantial data that supports deep learning, significantly advancing intelligent specific emitter identification (SEI) technology. However, challenges such as labeling costs and privacy concerns limit the availability of labeled samples, thereby constraining deep model training. To address this problem, this paper focuses on enhancing the data manifold structure through deep feature information, proposing a semi-supervised SEI method named SSME. A well-structured manifold makes the model capture underlying patterns and relationships within the data more effectively, leading to more accurate and generalizable classification boundaries. First, to maximize the use of supervision information from limited labeled samples, we design a supervised cross-class contrastive (SCCC) loss, which increases the feature distance between anchor samples and cross-class samples based on their labels, achieving better manifold separation of different categories. Second, we propose an instance neighborhood matching regularization (INMR) loss that captures the neighborhood of weakly and strongly augmented samples of unlabeled instances within the feature space. By aligning these neighborhood representations, neighborhood-to-neighborhood consistency learning is achieved, enhancing the structural consistency and smoothness of local manifolds. Evaluated on ADS-B and XSRP datasets across diverse settings, our method demonstrates superior performance over existing approaches. Notably, even with only five labeled samples per class, it surpasses supervised baselines by 24.82% and 12.55% on the respective datasets.