Data Augmentation Aided Few-Shot Learning for Specific Emitter Identification

Xixi Zhang, Yu Wang, Yibin Zhang, Yun Lin, Guan Gui, Ohtsuki Tomoaki, Hikmet Sari

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665454681
Publication statusPublished - 2022
Event96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022 - London, United Kingdom
Duration: 2022 Sept 262022 Sept 29

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


Conference96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022
Country/TerritoryUnited Kingdom


  • Data augmentation
  • deep learning
  • few-shot learning
  • specific emitter identification

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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