A Review of Specific Emitter Identification Based on Phase Space Reconstruction
Specific Emitter Identification (SEI), originated from identifying radar systems, is to extract fingerprint features from the intercepted signals for recognizing emitter identifies. Phase Space Reconstruction (PSR) is a powerful technique in time series analysis that can reconstruct a phase space fr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
China Science Publishing & Media Ltd. (CSPM)
2023-08-01
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| Series: | Leida xuebao |
| Subjects: | |
| Online Access: | https://radars.ac.cn/cn/article/doi/10.12000/JR23057 |
| _version_ | 1797422051218161664 |
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| author | Yurui ZHAO Zhitao HUANG Xiang WANG |
| author_facet | Yurui ZHAO Zhitao HUANG Xiang WANG |
| author_sort | Yurui ZHAO |
| collection | DOAJ |
| description | Specific Emitter Identification (SEI), originated from identifying radar systems, is to extract fingerprint features from the intercepted signals for recognizing emitter identifies. Phase Space Reconstruction (PSR) is a powerful technique in time series analysis that can reconstruct a phase space from a one-dimensional time series, preserving the nonlinear dynamic characteristics of the original system. The integration of phase space reconstruction into SEI began in 2007. However, due to the recent and diverse nature of research focused on PSR-based SEI methods, it is challenging to establish a clear context for its development. To address this issue, this paper aims to systematically summarize SEI methods based on phase space reconstruction. First, we introduce phase space reconstruction technology and emphasize the necessity and feasibility of applying it in SEI. Next, we present a comprehensive framework, classification, application, and comparison of PSR-based SEI methods. Simulation experiments demonstrated that PSR-based SEI methods can effectively describe the non-idealities of emitter hardware components and accomplish the target identification task. In addition, we verify that feature fusion enhances the algorithm’s robustness. Finally, we summarize the limitations of existing methods and outline prospects for future development. |
| first_indexed | 2024-03-09T07:26:32Z |
| format | Article |
| id | doaj.art-c14c04dcc8c441b09650aba0cf924def |
| institution | Directory Open Access Journal |
| issn | 2095-283X |
| language | English |
| last_indexed | 2024-03-09T07:26:32Z |
| publishDate | 2023-08-01 |
| publisher | China Science Publishing & Media Ltd. (CSPM) |
| record_format | Article |
| series | Leida xuebao |
| spelling | doaj.art-c14c04dcc8c441b09650aba0cf924def2023-12-03T07:03:42ZengChina Science Publishing & Media Ltd. (CSPM)Leida xuebao2095-283X2023-08-0112471373710.12000/JR23057R23057A Review of Specific Emitter Identification Based on Phase Space ReconstructionYurui ZHAO0Zhitao HUANG1Xiang WANG2College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaSpecific Emitter Identification (SEI), originated from identifying radar systems, is to extract fingerprint features from the intercepted signals for recognizing emitter identifies. Phase Space Reconstruction (PSR) is a powerful technique in time series analysis that can reconstruct a phase space from a one-dimensional time series, preserving the nonlinear dynamic characteristics of the original system. The integration of phase space reconstruction into SEI began in 2007. However, due to the recent and diverse nature of research focused on PSR-based SEI methods, it is challenging to establish a clear context for its development. To address this issue, this paper aims to systematically summarize SEI methods based on phase space reconstruction. First, we introduce phase space reconstruction technology and emphasize the necessity and feasibility of applying it in SEI. Next, we present a comprehensive framework, classification, application, and comparison of PSR-based SEI methods. Simulation experiments demonstrated that PSR-based SEI methods can effectively describe the non-idealities of emitter hardware components and accomplish the target identification task. In addition, we verify that feature fusion enhances the algorithm’s robustness. Finally, we summarize the limitations of existing methods and outline prospects for future development.https://radars.ac.cn/cn/article/doi/10.12000/JR23057specific emitter identification (sei)phase space reconstruction (psr)nonlinear dynamicsfingerprint featurestarget identification |
| spellingShingle | Yurui ZHAO Zhitao HUANG Xiang WANG A Review of Specific Emitter Identification Based on Phase Space Reconstruction Leida xuebao specific emitter identification (sei) phase space reconstruction (psr) nonlinear dynamics fingerprint features target identification |
| title | A Review of Specific Emitter Identification Based on Phase Space Reconstruction |
| title_full | A Review of Specific Emitter Identification Based on Phase Space Reconstruction |
| title_fullStr | A Review of Specific Emitter Identification Based on Phase Space Reconstruction |
| title_full_unstemmed | A Review of Specific Emitter Identification Based on Phase Space Reconstruction |
| title_short | A Review of Specific Emitter Identification Based on Phase Space Reconstruction |
| title_sort | review of specific emitter identification based on phase space reconstruction |
| topic | specific emitter identification (sei) phase space reconstruction (psr) nonlinear dynamics fingerprint features target identification |
| url | https://radars.ac.cn/cn/article/doi/10.12000/JR23057 |
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