Specific Emitter Identification via Bispectrum-Radon Transform and Hybrid Deep Model

Zhou, Yipeng; Wang, Xing; Chen, You; Tian, Yanling

doi:10.1155/2020/7646527

Cited by 19 publications

(9 citation statements)

References 40 publications

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“…where D l and D ul represent the labelled and unlabelled training sets, respectively; λ vadv > 0 represents the regularization coefficient that needs to be set in advance. Lðx l , y l ; θÞ represents the supervised loss function of the CNN, which is equivalent to Equation (6). Equation (10) shows that both labelled data and a large amount of unlabelled data are used to carry out semisupervised training.…”

Section: Virtual Adversarial Training (Vat)mentioning

confidence: 99%

“…However, this method is based on a linear transformation, which is not suitable for nonlinear radiation source signals. Zhou et al [6] proposed a feature extraction method based on the bispectrum-radon transform, which used bispectrum analysis to characterize the RF fingerprints and completed feature compression through radon transform. The method identified 6 ADS-B emitters with an accuracy of 90.25%.…”

Section: Introductionmentioning

confidence: 99%

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Virtual Adversarial Training-Based Semisupervised Specific Emitter Identification

Xie¹,

Zhang²,

Zhong

2022

Wireless Communications and Mobile Computing

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Deep learning is a new direction of research for specific emitter identification (SEI). Radio frequency (RF) fingerprints of the emitter signal are small and sensitive to noise. It is difficult to assign labels containing category information in noncooperative communication scenarios. This makes network models obtained by conventional supervised learning methods perform unsatisfactorily, leading to poor identification performance. To address this limitation, this paper proposes a semisupervised SEI algorithm based on bispectrum analysis and virtual adversarial training (VAT). Bispectrum analysis is performed on RF signals to enhance individual discriminability. A convolutional neural network (CNN) is used for RF fingerprint extraction. We used a small amount of labelled data to train the CNN in an adversarial manner to improve the antinoise performance of the network in a supervised model. Virtual adversarial samples were calculated for VAT, which made full use of labelled and large unlabelled training data to further improve the generalization capability of the network. Results of numerical experiments on a set of six universal software radio peripheral (USRP; model B210) devices demonstrated the stable and fast convergence performance of the proposed method, which exhibited approximately 90% classification accuracy at 10 dB. Finally, the classification performance of our method was verified using other evaluation metrics including receiver operating characteristic and precision-recall.

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Section: Virtual Adversarial Training (Vat)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Virtual Adversarial Training-Based Semisupervised Specific Emitter Identification

Xie¹,

Zhang²,

Zhong

2022

Wireless Communications and Mobile Computing

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“…Unintentional modulation is characteristic of the physical makeup of an emitter, so it is generally consistent throughout an emitted signal regardless of different data that the emitter might be transmitting at various times. The signed amplitude of the bispectrum of a discretely sampled complex signal x(t) at angular frequencies ω1 and ω2 is defined [8] as the Fourier transform of the third-order cumulant of x(t) as:…”

Section: Bispectramentioning

confidence: 99%

Arbitrarily Accurate Classification Applied to Specific Emitter Identification

Kleder¹

2022

Preprint

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<p>This article introduces a method of evaluating subsamples until any prescribed level of classification accuracy is attained, thus obtaining arbitrary accuracy. A logarithmic reduction in error rate is obtained with a linear increase in sample count. The technique is applied to specific emitter identification on a published dataset of physically recorded over-the-air signals from 16 ostensibly identical high-performance radios. The technique uses a multi-channel deep learning convolutional neural network acting on the bispectra of I/Q signal subsamples each consisting of 56 parts per million (ppm) of the original signal duration. High levels of accuracy are obtained with minimal computation time: in this application, each addition of eight samples decreases error by one order of magnitude.</p>

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“…However, this method is limited to signals with a communication preamble. A method for SEI based on the bispectrum-Radon transform was proposed in [9]. The method first estimates the bispectrum of the RF signal to preliminarily represent the RFFs and then compresses it via the Radon transform to obtain the bispectrum projection vector, which is used as the input of a hybrid network model to extract deep RFFs and conduct individual emitter identification.…”

Section: Introductionmentioning

confidence: 99%

Specific Emitter Identification with Limited Labelled Signals Based on Variational Autoencoder Embedded in Information-Maximising Generative Adversarial Network and Gradient Penalty

Xie¹,

Zhang²,

Zhong

2022

Wireless Communications and Mobile Computing

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The lack of labelled signal datasets in noncooperative scenarios limits the performance of specific emitter identification (SEI). To address this limitation, a method for SEI with limited labelled signals is proposed. The bispectrum of the received signal is estimated to enhance individual discriminability. An information-maximising generative adversarial network (InfoGAN) is then developed to perform SEI with limited labelled signals. To prevent nonconvergence and mode collapse due to the complexity of the radiofrequency signals, we improve the InfoGAN, respectively, from the generator and discriminator perspective. For the former, an encoder is combined with the InfoGAN generator to form a variational autoencoder that reduces the difficulty of convergence during training. For the latter, a gradient penalty algorithm is applied during the training of the InfoGAN discriminator, which enables its training loss function to obey the 1-Lipschitz constraint, thereby avoiding gradient disappearance. The design of the objective function for the training of each subnetwork and the training procedure are provided. The proposed network is trained with limited labelled and abundant unlabelled data, and an auxiliary classifier categorizes the emitters after training. Numerical results indicate that our method outperforms state-of-the-art algorithms for SEI with limited labelled signal samples in terms of effectiveness, convergence, accuracy, and robustness against noise.

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Specific Emitter Identification via Bispectrum-Radon Transform and Hybrid Deep Model

Cited by 19 publications

References 40 publications

Virtual Adversarial Training-Based Semisupervised Specific Emitter Identification

Virtual Adversarial Training-Based Semisupervised Specific Emitter Identification

Arbitrarily Accurate Classification Applied to Specific Emitter Identification

Specific Emitter Identification with Limited Labelled Signals Based on Variational Autoencoder Embedded in Information-Maximising Generative Adversarial Network and Gradient Penalty

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