Specific Emitter Identification Based on Variational Mode Decomposition and Spectral Features in Single Hop and Relaying Scenarios

Satija, Udit; Trivedi, Nikita; Biswal, Gagarin; Ramkumar, Barathram

doi:10.1109/tifs.2018.2855665

Cited by 140 publications

(79 citation statements)

References 35 publications

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“…However, in this study, we considered randomly varying SNR values within a range (not a single value), as this could better represent dynamic radio links of low power IoT devices, such as BT [18]. To this end, three different datasets with different SNR levels were created based on the ranges of SNR given in literature [6,7,15]: a) low SNR (-5 -0 dB), b) moderate SNR (0 -5 dB), and c) high SNR (5 -10 dB). Note that the distribution of SNR values of each dataset (and each device) were created to follow approximately Gaussian distribution in order to prove that the performance is evaluated with varying SNR values within the range.…”

Section: Data Collection (Signal Capturing) and Transient Detectionmentioning

confidence: 99%

“…It is computationally simple, and does not suffer from any mode mixing problem. Its superiority over EMD has been reported in various applications, such as the monitoring of wind turbines [14], single hop and relaying scenarios [15], fluctuation analysis [16], and pulse radar fingerprint extraction [17]. On the other hand, regarding the performance of VMD with transient signals, VMD has been successfully demonstrated with Bluetooth (BT) devices [18].…”

Section: Introductionmentioning

confidence: 99%

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On the Performance of Variational Mode Decomposition-Based Radio Frequency Fingerprinting of Bluetooth Devices

Aghnaiya

Dalveren

Kara

2020

Sensors

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Radio frequency fingerprinting (RFF) is one of the communication network's security techniques based on the identification of the unique features of RF transient signals. However, extracting these features could be burdensome, due to the nonstationary nature of transient signals. This may then adversely affect the accuracy of the identification of devices. Recently, it has been shown that the use of variational mode decomposition (VMD) in extracting features from Bluetooth (BT) transient signals offers an efficient way to improve the classification accuracy. To do this, VMD has been used to decompose transient signals into a series of band-limited modes, and higher order statistical (HOS) features are extracted from reconstructed transient signals. In this study, the performance bounds of VMD in RFF implementation are scrutinized. Firstly, HOS features are extracted from the band-limited modes, and then from the reconstructed transient signals directly. Performance comparison due to both HOS feature sets is presented. Moreover, the lower SNR bound within which the VMD can achieve acceptable accuracy in the classification of BT devices is determined. The approach has been tested experimentally with BT devices by employing a Linear Support Vector Machine (LSVM) classifier. According to the classification results, a higher classification performance is achieved (~4% higher) at lower SNR levels (−5-5 dB) when HOS features are extracted from band-limited modes in the implementation of VMD in RFF of BT devices.

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Section: Data Collection (Signal Capturing) and Transient Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the Performance of Variational Mode Decomposition-Based Radio Frequency Fingerprinting of Bluetooth Devices

Aghnaiya

Dalveren

Kara

2020

Sensors

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“…In general, the VMD algorithm is an adaptive, quasi-orthogonal and completely non-recursive decomposition method, consisting of classical Wiener Filtering, Hilbert Transform and frequency mixing. It decomposes the input signals composed of multi-components into several inherent modes with limited bandwidth, and most of these modes are closely around their corresponding central frequencies, which meet the definition of intrinsic mode functions (IMFs) [31].…”

Section: Variational Mode Decompositionmentioning

confidence: 99%

“…In these disturbances, the ripple is a very complex subject, and the ripple detection in DC links is critical for the evaluation of PQ of DC systems, because evaluating PQ reasonably and effectively is the first step in improving power supply quality [13], the quantification for ripple are convenient for the construction of DC PQ evaluation model. Currently, many methods for ripple detection of AC signal have been proposed [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32], such as Hilbert-Huang transform (HHT) [14,15], fast Fourier transform (FFT) [16][17][18], empirical mode decomposition (EMD) [19,20] and variational mode decomposition (VMD) [21,22] etc. The FFT algorithm is broadly utilized in industrial applications because of its fast and efficient advantages [16].…”

Section: Introductionmentioning

confidence: 99%

“…In the early stage, the VMD algorithm was widely utilized in rotating machinery fault diagnosis, transformer fault diagnosis, lightning fault location of high voltage transmission lines [30]. Recently some VMD algorithms focused on the disturbances signal of AC PQ are reported in [31,32]. The VMD and decision tree are utilized to detect and classify the disturbance signals of AC PQ, which performs good performance in detecting and analyzing the non-stationary disturbance signals of AC signal, including the fundamental, harmonics, interharmonics [32].…”

Section: Introductionmentioning

confidence: 99%

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Application of VMD and Hilbert Transform Algorithms on Detection of the Ripple Components of the DC Signal

Luo

et al. 2020

Energies

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Accurate detection of ripple components of the direct-current (DC) signals is essential for evaluating DC power quality. In this study, the combination algorithm based on variational mode decomposition (VMD) and Hilbert transform (HT) is applied to detect and analyze the characteristics of the ripple components of the DC disturbance signals. Firstly, the optimal modal number of VMD algorithms is comprehensively determined by observing the center frequencies of the mode components and the Index of Orthogonality (IO) of mode components. Through utilizing the VMD algorithm, the DC disturbance signal is accurately decomposed into a series of amplitude modulation-frequency modulation (AM-FM) functions. Then, the HT algorithm is applied to each AM-FM function to obtain the corresponding instantaneous amplitude and frequency, and the characteristics of DC disturbance signal are determined. Some case studies are implemented to analyze the ripple components of the DC disturbance signal with the VMD-HT and empirical mode decomposition (EMD) algorithm. Finally, the experiment results of Gree Photovoltaic Cabin have verified the feasibility and effectiveness of the proposed combination VMD-HT algorithm by comparison with EMD and the window interpolation fast Fourier transform (WIFFT) algorithms.

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Specific Emitter Identification Based on ACO-XGBoost Feature Selection

Chen

Wang

et al. 2023

Lecture Notes in Computer Science

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Specific Emitter Identification Based on Variational Mode Decomposition and Spectral Features in Single Hop and Relaying Scenarios

Cited by 140 publications

References 35 publications

On the Performance of Variational Mode Decomposition-Based Radio Frequency Fingerprinting of Bluetooth Devices

On the Performance of Variational Mode Decomposition-Based Radio Frequency Fingerprinting of Bluetooth Devices

Application of VMD and Hilbert Transform Algorithms on Detection of the Ripple Components of the DC Signal

Specific Emitter Identification Based on ACO-XGBoost Feature Selection

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