Non-Exemplar Class-Incremental Learning via Prototype Correction and Hierarchical Regularization for Specific Emitter Identification

Specific Emitter Identification (SEI) is a non-encrypted authentication technology that adds a layer of security to wireless communications in intelligent transportation systems. However, most existing SEI methods are constrained to identify fixed classes and cannot learn incrementally. In practical applications, the constant emergence of new classes or tasks necessitates the capacity to continuously learn new classes from streaming data. In this paper, combining momentum-based prototype correction and hierarchical regularization, a simple and effective non-example class-incremental method for SEI is proposed, named MoPC-HR. The momentum-based prototype correction dynamically adjusts old class prototypes (i.e., old class centers), strengthening the separation between old and new classes and enabling smoother integration of new classes. Hierarchical regularization is applied at multiple levels to control feature differences between old and new classes, preventing the model from favoring new classes and reducing catastrophic forgetting. Experiments on real-world AIS-100 and ADS-B datasets show that MoPC-HR outperforms state-of-the-art methods. Specifically, in the incremental phase 20, MoPC-HR achieves an average accuracy of 97.04%, average forgetting of 2.95%, and average intransigence of 2.85% on the AIS-100 dataset; and an average accuracy of 95.99%, average forgetting of 3.66%, and average intransigence of 3.60% on the ADS-B dataset. The code is available at https://github.com/xmuLdz/MoPC-HR.git