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Feature Analysis and Extraction for Specific Emitter Identification Based on the Signal Generation Mechanisms of Radar Transmitters.
Liu, Yilin; Li, Shengyong; Lin, Xiaohong; Gong, Hui; Li, Hongke.
Afiliación
  • Liu Y; Department of Electronic Technology, Naval University of Engineering, Wuhan 430033, China.
  • Li S; Department of Electronic Technology, Naval University of Engineering, Wuhan 430033, China.
  • Lin X; Department of Electronic Technology, Naval University of Engineering, Wuhan 430033, China.
  • Gong H; Department of Electronic Technology, Naval University of Engineering, Wuhan 430033, China.
  • Li H; Department of Electronic Technology, Naval University of Engineering, Wuhan 430033, China.
Sensors (Basel) ; 22(7)2022 Mar 29.
Article en En | MEDLINE | ID: mdl-35408230
ABSTRACT
In this study, a feature analysis and extraction method was proposed for specific emitter identification based on the signal generation mechanisms of radar transmitters. The generation of radar signals by radar transmitters was analyzed theoretically and experimentally. In the analysis, the main source of unintentional modulation in radar signals was identified, and the frequency stabilization of the solid-state frequency source, the nonlinear characteristics of the radio frequency amplifier chain, and the envelope of the pulse front edge were extracted as features for specific emitter identification. Subsequently, these characteristics were verified through simulation. The results revealed that the features extracted by this method exhibit "fingerprint characteristics" and can be used to identify specific radar emitters.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Radar / Algoritmos Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Radar / Algoritmos Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article País de afiliación: China