scispace - formally typeset
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
More filters
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.
Journal ArticleDOI

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

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
More filters
Journal ArticleDOI

Variational Mode Decomposition

TL;DR: This work proposes an entirely non-recursive variational mode decomposition model, where the modes are extracted concurrently and is a generalization of the classic Wiener filter into multiple, adaptive bands.
Journal ArticleDOI

Musical genre classification of audio signals

TL;DR: The automatic classification of audio signals into an hierarchy of musical genres is explored and three feature sets for representing timbral texture, rhythmic content and pitch content are proposed.
Journal ArticleDOI

Survey of automatic modulation classification techniques: classical approaches and new trends

TL;DR: The authors provide a comprehensive survey of different modulation recognition techniques in a systematic way, and simulated some major techniques under the same conditions, which allows a fair comparison among different methodologies.
Journal ArticleDOI

A Survey on the Application of Genetic Programming to Classification

TL;DR: This paper surveys existing literature about the application of genetic programming to classification, to show the different ways in which this evolutionary algorithm can help in the construction of accurate and reliable classifiers.
Journal ArticleDOI

On the likelihood-based approach to modulation classification

TL;DR: Findings show that the HLRT suffers from very high complexity, whereas the QHLRT provides a reasonable solution, and an upper bound on the performance of QHL RT-based algorithms, which employ unbiased and normally distributed non-data aided estimates of the unknown parameters, is proposed.
Related Papers (5)