Specific emitter individual identification based on the characteristics of synchronization signal rising edges and deep neural network

Peng Yan; Baigang Huang; Jianliang Xu; Yan Chen; Xiazhao Zhang; Jinyang Song

doi:10.1117/12.2653016

19 December 2022 Specific emitter individual identification based on the characteristics of synchronization signal rising edges and deep neural network

Peng Yan, Baigang Huang, Jianliang Xu, Yan Chen, Xiazhao Zhang, Jinyang Song

Author Affiliations +

Proceedings Volume 12321, Advanced Sensor Systems and Applications XII; 123211G (2022) https://doi.org/10.1117/12.2653016
Event: SPIE/COS Photonics Asia, 2022, Online Only

Conference Poster

Abstract

Specific emitter identification technology has always been a hot issue for the vital research of radio-related departments in various countries. However, due to the sharp increase in the types of radiation sources and the complexity of electromagnetic space, identifying individual radiation sources has become more challenging. In order to provide a convenient and effective radiation source identification method, this paper proposes an emitter individual identification research method based on the characteristics of synchronization signal rising edges. With the help of artificial intelligence technology, the method proposed in this paper has achieved a very high individual identification rate of radiation sources by using the characteristics of synchronization signal rising edges and deep neural networks.

Citation Download Citation

Peng Yan, Baigang Huang, Jianliang Xu, Yan Chen, Xiazhao Zhang, and Jinyang Song "Specific emitter individual identification based on the characteristics of synchronization signal rising edges and deep neural network", Proc. SPIE 12321, Advanced Sensor Systems and Applications XII, 123211G (19 December 2022); https://doi.org/10.1117/12.2653016

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