Meta-RFF: Meta-Task Adaptive Based Few-Shot Open-Set Incremental Learning for RF Fingerprint Recognition

In recent years, deep learning (DL) techniques have been extensively utilized for specific emitter identification through the extraction of RF fingerprints. A significant challenge that DL models face in real-world scenarios is the continuous emergence of new wireless devices, such as unknown drones that suddenly appear in the sky. In these situations, the radio monitoring system must be capable of detecting these unknown devices (open-set recognition) and incrementally updating the DL models knowledge using only a few captured samples. This requirement presents two main challenges: 1) Incremental updates from fewshot samples can lead to catastrophic forgetting and overfitting in DL models; 2) Constructing reliable open-set thresholds for new devices with few-shot samples is difficult. To address these challenges, we propose a novel few-shot openset incremental learning (FSOSIL) framework through metalearning for RF fingerprint recognition, named Meta–RFF. The core idea of Meta–RFF is to simulate few-shot incremental learning and open-set recognition scenarios by constructing numerous pseudo-FSOSIL tasks and meta-training them. To enhance the open-set recognition capability, we design RF feature augmentation, open loss, and adaptive open-set thresholding modules. The algorithms effectiveness is validated on the largescale aircraft recognition dataset (ADS-B), showing an improvement in closed-set accuracy and open-set AUROC of the new class by approximately 10-20% compared to other algorithms with 1-shot. We also demonstrate the algorithms effectiveness in a real-world test bed.