FSCIL-SEI: Few-Shot Class-Incremental Learning Approach for Specific Emitter Identification

Abstract

—Specific emitter identification (SEI) is a non-password authentication method that adds an extra layer of security to wireless devices. However, existing SEI methods are unable to continuously learn new classes from a limited number of training examples due to data scarcity, which is more challenging than the catastrophic forgetting and overfitting problems associated with the widely studied class-incremental learning (CIL). In this article, we propose a novel few-shot class-incremental specific emitter identification (FSCIL-SEI) framework to address the challenge of catastrophic forgetting and overfitting in CIL. Specifically, to ensure interclass discriminability during the incremental process, we first employ prototype learning training methods in the base task and introduce a self-supervised contrastive learning (SSCL) that increases interclass distances and reduces intraclass distances in the feature space. Second, we propose a separation of class weights (SCWs) to isolate old and new class weights in the classification layer, which effectively mitigates the issue of catastrophic forgetting. Finally, to alleviate the problem of overfitting due to insufficient samples during incremental training, we introduce a three-stage course learning (CL) approach that advances from simple to complex tasks, which not only mitigates overfitting but also improves the generalization ability of the model. Experimental results demonstrate that our method outperforms other FSCIL methods in terms of both performance degradation (PD) and incremental accuracy when evaluated on automatic identification system (AIS) and automatic dependent surveillance–broadcast (ADS-B) datasets

Tasks

Reproductions