Journal ArticleDOI
Specific Emitter Identification Based on Variational Mode Decomposition and Spectral Features in Single Hop and Relaying Scenarios
TLDR
This paper develops an emitter identification based on variational mode decomposition and spectral features (VMD-SF), which outperforms the proposed VMD-<inline-formula> <tex-math notation="LaTeX">$EM^{2}$ </tex-Math></inline- formula> method and has lowest computational cost as compared with the aforementioned methods.Abstract:
Specific emitter identification is the process of identifying or discriminating different emitters based on the radio frequency fingerprints extracted from the received signal. Due to inherent non-linearities of the power amplifiers of emitters, these fingerprints provide distinguish features for emitter identification. In this paper, we develop an emitter identification based on variational mode decomposition and spectral features (VMD-SF). As VMD decomposes the received signal simultaneously into various temporal and spectral modes, we choose to explore different spectral features, including spectral flatness, spectral brightness, and spectral roll-off for improving the identification accuracy contrary to existing temporal features-based methods. For demonstrating the robustness of VMD in decomposing the received signal into emitter-specific modes, we also develop a VMD-entropy and moments ( $EM^{2}$ ) method based on existing temporal features extracted from the Hilbert Huang transform of the emitter-specific temporal modes. Our proposed method has three major steps: received signal decomposition using VMD, feature extraction, and emitter identification. We evaluate the performance of the proposed methods using the probability of correct classification ( $P_{cc}$ ) both in single hop and in relaying scenario by varying the number of emitters. To demonstrate the superior performance of our proposed methods, we compared our methods with the existing empirical mode decomposition-(entropy-, first-, and second-order moments) (EMD- $EM^{2}$ ) method both in terms of $P_{cc}$ and computational complexity. Results depict that the proposed VMD-SF emitter identification method outperforms the proposed VMD- $EM^{2}$ method and the existing EMD- $EM^{2}$ method both in single hop and relaying scenarios for a varying number of emitters. In addition, the proposed VMD-SF method has lowest computational cost as compared with the aforementioned methods.read more
Citations
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Journal ArticleDOI
Towards Scalable and Channel-Robust Radio Frequency Fingerprint Identification for LoRa
TL;DR: In this paper , a scalable and channel-robust RFFI framework achieved by deep learning powered radio frequency fingerprint (RFF) extractor and channel independent features was proposed. But the authors did not consider the channel-independent features and data augmentation.
Proceedings ArticleDOI
Wireless Device Identification Based on Radio Frequency Fingerprint Features.
TL;DR: Both power spectral density (PSD) and fractional Fourier transform (FrFT) methods are used to extract the characteristics of transient signals and the SEI system model is constructed based on these techniques.
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ECG Arrhythmia Classification using High Order Spectrum and 2D Graph Fourier Transform
TL;DR: In this study, a feature extraction method based on the bispectrum and 2D graph Fourier transform (GFT) was developed, which achieved a high classification accuracy of 96.2%.
Journal ArticleDOI
Broadband Mode Decomposition and Its Application to the Quality Evaluation of Welding Inverter Power Source Signals
TL;DR: Simulation and experimental signal analyses indicate that B MD is more accurate than EEMD and VMD in extracting broadband components from a noisy signal and that BMD is suitable for quality evaluations of welding inverter power source signals.
Journal ArticleDOI
Mutation grey wolf elite PSO balanced XGBoost for radar emitter individual identification based on measured signals
Zhao Shiqiang,Deguo Zeng,Wang Wenhai,Xinwei Chen,Zeyin Zhang,Fuyuan Xu,Mao Xuanyu,Xinggao Liu +7 more
TL;DR: Results verify that MGWEPSO-BXGBoost has high accuracy and strong stability even when the sample size of each individual is limited and imbalanced, which is conducive to improving the ability to find the global optimal solution.
References
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Musical genre classification of audio signals
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