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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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Proceedings ArticleDOI

Mag-Auth: Authenticating Wireless Transmitters and Receivers on the Receiver Side via Magnetic Emissions

TL;DR: Mag-Auth as mentioned in this paper leverages the EM emissions released at the joint connection between the wireless device and its antenna in response to an excitation signal to achieve low-cost, crypto-less authentication.
Journal ArticleDOI

Radio frequency fingerprinting feature fusion based on multi-domain discriminant kernel canonical correlation analysis

丽婷 孙, +2 more
- 01 Jan 2023 - 
TL;DR: In this paper , a multi-domain feature fusion strategy based on discriminant kernel canonical correlation analysis (MDKCCA) is proposed for specific emitter identification (SEI) which fully exploits the complementarity between the features of different domains and combines feature tag information.
Journal ArticleDOI

Channel identification with Improved Variational Mode Decomposition

TL;DR: In this paper , the authors proposed the combination of machine learning with a recently proposed signal processing tool called Variational Mode Decomposition (VMD), which is a decomposition algorithm that decomposes a time series into several modes which have specific sparsity properties.
Journal ArticleDOI

Open-Set Specific Emitter Identification Based on Prototypical Networks and Extreme Value Theory

TL;DR: Wang et al. as discussed by the authors proposed an open set recognition model based on prototypical networks and extreme value theory to solve the problem of specific emitter identification in open-set scenes and further improve the recognition accuracy and robustness.
Journal ArticleDOI

Balanced Neural Architecture Search and Its Application in Specific Emitter Identification

TL;DR: In this article, a variable network architecture search (NAS) mechanism, called balanced-NAS framework, was proposed for specific emitter identification (SEI) to greatly improve the flexibility of model searching.
References
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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.
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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.
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