Specific Emitter Identification Based on Multi-Scale Multi-Dimensional Approximate Entropy
Addressing the computational demands and data requirements associated with deep learning techniques, this study presents a novel Specific Emitter Identification (SEI) strategy, based on Multi-Scale Multi-Dimensional Approximate Entropy (MSMD-AE). We focus on the steady-state segment of received sign...
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Published in | IEEE signal processing letters Vol. 31; pp. 850 - 854 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
New York
IEEE
2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
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Summary: | Addressing the computational demands and data requirements associated with deep learning techniques, this study presents a novel Specific Emitter Identification (SEI) strategy, based on Multi-Scale Multi-Dimensional Approximate Entropy (MSMD-AE). We focus on the steady-state segment of received signals, obtained through Katz Fractal Dimension (KFD). The performance of proposed method is thoroughly evaluated across a range of SNR variations for two distinct scenarios, involving real-world Very High-Frequency (VHF) radios and open-source cell phone datasets. A comprehensive comparison with the most relevant literature exhibits the superior performance of proposed MSMD-AE method. |
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ISSN: | 1070-9908 1558-2361 |
DOI: | 10.1109/LSP.2024.3375264 |