A Multigranularity Embedding Guided Open-Set Recognition for Fine-Grained Specific Emitter Identification

This paper presents MGEGOR, a new AI method for specific emitter identification (SEI, the process of authenticating wireless devices by analyzing their unique transmission characteristics). The method improves on existing approaches by better identifying both known devices seen during training and unknown devices that were not part of the training data, which is important for security in open-set conditions (scenarios where new, unauthorized devices may appear). The framework uses contrastive representation learning (a technique where the AI learns by comparing similar and dissimilar examples) and prototype-based embedding (storing representative examples of device types) to work effectively even when conditions change over time.