A Compact Manifold Mixup Feature-Based Open-Set Recognition Approach for Unknown Signals

There are all kinds of unknown and known signals in the actual electromagnetic environment, which hinders the development of practical cognitive radio applications. However, most existing signal recognition models are difficult to discover unknown signals while recognizing known ones. In this paper, a compact manifold mixup feature-based open-set recognition approach (OR-CMMF) is proposed to address the above problem. First, the proposed approach utilizes the center loss to constrain decision boundaries so that it obtains the compact latent signal feature representations and extends the low-confidence feature space. Second, the latent signal feature representations are used to construct synthetic representations as substitutes for unknown categories of signals. Then, these constructed representations can occupy the extended low-confidence space. Finally, the proposed approach applies the distillation loss to adjust the decision boundaries between the known categories signals and the constructed unknown categories substitutes so that it accurately discovers unknown signals. The OR-CMMF approach outperformed other state-of-the-art open-set recognition methods in com prehensive recognition performance and running time, as demonstrated by simulation experiments on two public datasets RML2016.10a and ORACLE.