Few-Shot Specific Emitter Identification Based on a Contrastive Masked Learning Framework

Abstract

Specific emitter identification (SEI) is a unique physical-layer security technology that plays a crucial role in protecting wireless communication systems from various security threats. Although SEI based on artificial neural network models has achieved good identification performance, its performance degrades when labeled samples are limited. To address this issue, this letter proposes a few-shot SEI method based on a contrastive masked learning framework. This method combines contrastive learning and masked learning to enhance the model’s representation capability, and it consists of an encoder, a signal decoder, a feature decoder, and a momentum encoder. Simulation experiments on the open-source datasets LoRa and ADS-B show that the proposed method outperforms other SEI methods.