A novel specific emitter identification method based on radio frequency fingerprints

Deng, Shouyun; Huang, Zhitao; Wang, Xiang

doi:10.1109/ciapp.2017.8167241

Cited by 14 publications

(4 citation statements)

References 7 publications

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“…In [ 7 , 10 ], the authors used the skewness and kurtosis algorithm to extract fingerprint features of a radiation source hidden in the signal. The authors in [ 10 ] used EMD, ITD, and VMD to decompose the signal, and then extracted the skewness and kurtosis information of each component as the fingerprint feature of the radiation source.…”

Section: Simulation and Experiments Resultsmentioning

confidence: 99%

“…For limited samples, linear moment estimation is more robust and accurate than other estimation methods are, and even better than maximum-likelihood estimation. Therefore, linear skewness and linear kurtosis are not sensitive to outliers, so non-Gaussian high-precision measurement can be achieved [ 7 ]. Research on radiation-source identification methods based on statistical time-domain characteristics is at a relatively early stage, but these methods are susceptible to noise; in this case, they are not enough to analyze non-Gaussian and nonstationary signals.…”

Section: Introductionmentioning

confidence: 99%

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Emitter Identification of Digital Modulation Transmitter Based on Nonlinearity and Modulation Distortion of Power Amplifier

Chen

Lei

2021

Sensors

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Specific transmitter identification (SEI) is a technology that uses a received signal to identify to which individual radiation source the transmitted signal belongs. It can complete the identification of the signal transmitter in a non-cooperative scenario. Therefore, there are broad application prospects in the field of wireless-communication-network security, spectral resource management, and military battlefield-target communication countermeasures. This article demodulates and reconstructs a digital modulation signal to obtain a signal without modulator distortion and power-amplifier nonlinearity. Comparing the reconstructed signal with the actual received signal, the coefficient representation of the nonlinearity of the power amplifier and the distortion of the modulator can be obtained, and these coefficients can be used as the fingerprint characteristics of different transmitters through a convolutional neural network (CNN) to complete the identification of specific transmitters. The existing SEI strategy for changing the modulation parameters of a test signal is to mix part of the test signal with the training signal so that the classifier can learn the signal of which the modulation parameter was changed. This method is still data-oriented and cannot process signals for which the classifier has not been trained. It has certain limitations in practical applications. We compared the fingerprint features extracted by the method in this study with the fingerprint features extracted by the bispectral method. When SNR < 20 dB, the recognition accuracy of the bispectral method dropped rapidly. The method in this paper still achieved 86% recognition accuracy when SNR = 0 dB. When the carrier frequency of the test signal was changed, the bispectral feature failed, and the proposed method could still achieve a recognition accuracy of about 70%. When changing the test-signal baud rate, the proposed method could still achieve a classification accuracy rate of more than 70% for four different individual radiation sources when SNR = 0 dB.

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Section: Simulation and Experiments Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Emitter Identification of Digital Modulation Transmitter Based on Nonlinearity and Modulation Distortion of Power Amplifier

Chen

Lei

2021

Sensors

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“…Through the extraction of subtle features, often arising from variances in hardware circuitry such as modulators, power amplifiers, and transmitters, within radio frequency signals, radar emitters are identified. Previous research relied on manual feature extraction [20][21][22]: encompassing attributes like pulse width, pulse repetition interval (PRI), leading-edge slope, and rise time. However, this approach heavily relies on expert experience.…”

Section: Related Workmentioning

confidence: 99%

Research on an Enhanced Multimodal Network for Specific Emitter Identification

Peng,

Xie,

Zou

2024

Electronics

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Specific emitter identification (SEI) refers to the task of distinguishing similar emitters, especially those of the same type and transmission parameters, which is one of the most critical tasks of electronic warfare. However, SEI is still a challenging task when a feature has low physical representation. Feature representation largely determines the recognition results. Therefore, this article expects to move toward robust feature representation for SEI. Efficient multimodal strategies have great potential for applications using multimodal data and can further improve the performance of SEI. In this research, we introduce a multimodal emitter identification method that explores the application of multimodal data, time-series radar signals, and feature vector data to an enhanced transformer, which employs a conformer block to embed the raw data and integrates an efficient multimodal feature representation module. Moreover, we employ self-knowledge distillation to mitigate overconfident predictions and reduce intra-class variations. Our study reveals that multimodal data provide sufficient information for specific emitter identification. Simultaneously, we propose the CV-CutMixOut method to augment the time-domain signal. Extensive experiments on real radar datasets indicate that the proposed method achieves more accurate identification results and higher feature discriminability.

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“…Machine learning (ML) is a subset of artificial intelligence (AI) that emerged from pattern recognition [ 10 ]. Lately, research in wireless communication has noted the distinction and effectiveness of machine learning by identifying the probability of learning based on signal classification [ 11 ] and specific emitter identification [ 12 , 13 ]. However, ML algorithms may face difficulty handling high-dimensional data because of the sizeable signals of raw data.…”

Section: Introductionmentioning

confidence: 99%

Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches

Alhoraibi

Alghazzawi

Alhebshi

et al. 2023

Sensors

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The physical layer security of wireless networks is becoming increasingly important because of the rapid development of wireless communications and the increasing security threats. In addition, because of the open nature of the wireless channel, authentication is a critical issue in wireless communications. Physical layer authentication (PLA) is based on distinctive features to provide information-theory security and low complexity. However, although many researchers are interested in the PLA and how it might be used to improve wireless security, there is surprisingly little literature on the subject, with no systematic overview of the current state-of-the-art PLA and the main foundations involved. Therefore, this paper aims to determine and systematically compare existing studies in the physical layer authentication. This study showed whether machine learning approaches in physical layer authentication models increased wireless network security performance and demonstrated the latest techniques used in PLA. Moreover, it identified issues and suggested directions for future research. This study is valuable for researchers and security model developers interested in using machine learning (ML) and deep learning (DL) approaches for PLA in wireless communication systems in future research and designs.

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A novel specific emitter identification method based on radio frequency fingerprints

Cited by 14 publications

References 7 publications

Emitter Identification of Digital Modulation Transmitter Based on Nonlinearity and Modulation Distortion of Power Amplifier

Emitter Identification of Digital Modulation Transmitter Based on Nonlinearity and Modulation Distortion of Power Amplifier

Research on an Enhanced Multimodal Network for Specific Emitter Identification

Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches

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