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YAN Wenjun, LIU Kangsheng, LING Qin, et al. Survey of cross-scenario specific emitter identification technology[J]. Journal of Radars, in press. doi: 10.12000/JR25166
Citation: YAN Wenjun, LIU Kangsheng, LING Qin, et al. Survey of cross-scenario specific emitter identification technology[J]. Journal of Radars, in press. doi: 10.12000/JR25166
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Survey of Cross-Scenario Specific Emitter Identification Technology

DOI: 10.12000/JR25166 CSTR: 32380.14.JR25166

  • Naval Aviation University, Yantai 264001, China

Funds:  The National Natural Science Foundation of China (62371465), The Taishan Scholars Project Special Fund (ts201511020), Youth Innovation Teams in Shandong Province Fund (2022KJ084)
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    Abstract
  • Specific emitter identification (SEI) relies on subtle differences in the radio-frequency fingerprints of device-emitted signals to determine the emitter identity attributes. SEI plays a fundamental role in wireless security, spectrum management, and situational awareness. However, as wireless scenarios become increasingly diverse and dynamic, deep learning models trained in a single domain (where the source and target domains share the same distribution) often suffer severe performance degradation in real-world settings such as cross-receiver and cross-time scenarios. This degradation has not yet been comprehensively analyzed. To address this issue, this paper first classifies SEI according to cross-scenario types, and then systematically reviews mainstream algorithm frameworks and representative SEI methods, with a particular focus on the core ideas and key technologies underlying each method. It also summarizes the main open-source cross-scenario SEI datasets. Finally, the paper identifies current research bottlenecks and outlines potential future directions, aiming to facilitate advances in SEI theories and methodologies applicable to complex electromagnetic environments.

     

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