Specific emitter identification (SEI) extracts the fingerprint characteristics of emitters according to the subtle differences of transmitted signals, to distinguish different emitter individuals and prevent unauthorized network access. Deep learning (DL) based SEI methods have been proposed to achieve a good identification performance in recent years. However, the existing methods need a massive specific emitter dataset to alleviate model overfitting during the training stage. In this paper, we propose data augmentation (DA) aided few-shot learning method and validate the proposed method using automatic dependent surveillance-broadcast (ADS-B) signals. Specifically, according to the characteristics of ADS-B signals, four DA methods, i.e., flip, rotation, shift, and noise are studied for the proposed method. Experimental results are provided to show that the proposed method improves the recognition accuracy and the model robustness.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Data Augmentation Aided Few-Shot Learning for Specific Emitter Identification


    Contributors:
    Zhang, Xixi (author) / Wang, Yu (author) / Zhang, Yibin (author) / Lin, Yun (author) / Gui, Guan (author) / Tomoaki, Ohtsuki (author) / Sari, Hikmet (author)


    Publication date :

    2022-09-01


    Size :

    669142 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Distributed Unknown Specific Emitter Identification Based on Federated Learning

    Xiao, Hongyujie / Liu, Heng / Zhou, Yi et al. | IEEE | 2024


    A Survey of Specific Emitter Identification

    Li, Y. / Qin, X. | TIBKAT | 2023



    IRelNet: An Improved Relation Network for Few-Shot Radar Emitter Identification

    Zilong Wu / Meng Du / Daping Bi et al. | DOAJ | 2023

    Free access