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Specific Emitter Identification Based on Multi-Scale Multi-Dimensional Approximate Entropy
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DOI：10.1109/LSP.2024.3375264.png)
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Abstract
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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.
Keywords:
Fractal dimension
multi dimension approximate entropy
multi scale approximate entropy
specific emitter identification
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