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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.

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Citations
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Journal ArticleDOI

Specific Emitter Identification Based on Deep Residual Networks

TL;DR: A novel SEI algorithm using deep learning architecture that combines high information integrity with low complexity, which outperforms previous studies in the literature and has the capability of adapting to signals collected under various conditions.
Journal ArticleDOI

Cooperative Specific Emitter Identification via Multiple Distorted Receivers

TL;DR: Simulation results show that the proposed multi-receiver cooperative schemes can achieve the diversity gain in the identification performance, and the receive diversity can be achieved by the proposed schemes by using multiple distorted receivers even without compensating the receiver distortion prior to the identification.
Journal ArticleDOI

Unsupervised Specific Emitter Identification Method Using Radio-Frequency Fingerprint Embedded InfoGAN

TL;DR: Numerical results indicate that the proposed framework consistently outperforms state-of-the-art algorithms for unsupervised SEI applications, both in terms of evaluation score and classification accuracy.
Journal ArticleDOI

Radio Frequency Fingerprint Identification for LoRa Using Deep Learning

TL;DR: A hybrid classifier that can adjust the prediction of deep learning models with the estimated CFO is designed to further increase the classification accuracy of the deep learning-based RFFI scheme for Long Range (LoRa) systems.
Journal ArticleDOI

Radio Frequency Fingerprint Identification for Narrowband Systems, Modelling and Classification

TL;DR: A convolutional neural network-based RFFI protocol is proposed that can classify 50 and 200 devices with uniformly and randomly distributed IQ imbalances and PA nonlinearities with high accuracy and has some tolerance on different receiver imbalance during training and classification.
References
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Journal ArticleDOI

Wavelet Fingerprinting of Radio-Frequency Identification (RFID) Tags

TL;DR: Here, RF fingerprinting is implemented to authenticate individual RFID tags at the physical layer using the dynamic wavelet fingerprint and supervised pattern classification techniques to identify uniqueRFID tags with up to 99% accuracy.
Journal ArticleDOI

Fast and Robust Modulation Classification via Kolmogorov-Smirnov Test

TL;DR: Compared with the traditional cumulant-based classifiers, the proposed K-S classifiers offer superior classification performance, require less number of signal samples (thus is fast), and is more robust to various channel impairments.
Journal ArticleDOI

Specific Emitter Identification via Hilbert–Huang Transform in Single-Hop and Relaying Scenarios

TL;DR: This paper investigates the specific emitter identification (SEI) problem, which distinguishes different emitters using features generated by the nonlinearity of the power amplifiers of emitters, and three algorithms based on the Hilbert spectrum are proposed that show effectiveness in both single-hop and relaying scenarios, as well as under different channel conditions.
Journal ArticleDOI

A new feature vector using selected bispectra for signal classification with application in radar target recognition

TL;DR: Since the selected bispectra of range profiles are translation invariant and can avoid redundant and baneful bispecta as features, they are especially suitable for radar target recognition, which is shown by experiments.
Journal ArticleDOI

Digital channelized receiver based on time-frequency analysis for signal interception

TL;DR: A digital channelized receiver is presented for the interception of a wide variety of signals of complex structure, including those with low probability of interception, and shows a good performance in terms of detection, estimation, and processing of simultaneous signals.
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