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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

Specific Emitter Identification Based on Joint Wavelet Packet Analysis

TL;DR: In this paper , the authors proposed a joint wavelet packet decomposition and support vector machine (SVM) based specific emitter identification (SEI) algorithm for low SNR regime.
Journal ArticleDOI

Variable-Modulation Specific Emitter Identification With Domain Adaptation

TL;DR: In this article , a variable-modulation SEI framework with domain adaptation is proposed to cope with the daunting challenge, where the components characteristics of transmitter are analyzed and the distortion models are established for simulation dataset generation.
Proceedings ArticleDOI

Specific Emitter Identification Based on CNN via Variational Mode Decomposition and Bimodal Feature Fusion

Liu Yang
TL;DR: Wang et al. as mentioned in this paper proposed an emitter identification scheme based on convolutional neural network (CNN) via variational mode decomposition (VMD) and bimodal feature fusion.
Journal ArticleDOI

GPU-Free Specific Emitter Identification Using Signal Feature Embedded Broad Learning

TL;DR: In this paper , a GPU-free SEI method using a signal feature embedded broad learning network (SFEBLN) was proposed for efficient emitter identification based on a single-layer forward propagation network on the CPU platform.
Journal ArticleDOI

An End-to-End Deep Learning Approach for State Recognition of Multifunction Radars

Xinsong Xu, +2 more
- 01 Jul 2022 - 
TL;DR: This work focuses on the MFR state recognition with actual intercepted MFR signals and proposes a novel end-to-end state recognition approach with two RNNs’ connections that makes full use of RNN’ ability to directly tackle corrupted data and automatically learn the features from input data.
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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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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.
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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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