Specific Emitter Identification Based on Multi-Level Sparse Representation in Automatic Identification System

Qian, Yunhan; Qi, Jie; Kuai, Xiaoyan; Han, Guangjie; Sun, Haixin; Hong, Shaohua

doi:10.1109/tifs.2021.3068010

Cited by 73 publications

(25 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on network models such as CNN and LSTM, some new deep learning-based network models and algorithms for SEI have been proposed recently. Qian et al [17] proposed an approach of multilevel sparse representationbased identification for SEI, which comprehensively used the CNN for RF fingerprint extraction and principal component analysis for sparse representation. The method can classify nine transmitters with a classification accuracy of over 90% using a small number of training samples.…”

Section: Introductionmentioning

confidence: 99%

Virtual Adversarial Training-Based Semisupervised Specific Emitter Identification

Xie¹,

Zhang²,

Zhong

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Virtual Adversarial Training-Based Semisupervised Specific Emitter Identification

Xie¹,

Zhang²,

Zhong

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Recently, considering the characteristics of SEI application, scholars have proposed some new network models and algorithms for SEI using basic network models, such as CNNs and LSTM networks. Qian et al [28] proposed an automatic SEI system based on a CNN with multilevel sparse representation. The SEI system splices the shallow and deep RFF features extracted by the CNN and then performs SEI based on the sparse representation 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

View full text Add to dashboard Cite

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.

show abstract

“…Therefore, a large number of features based on steady-state signals have been proposed, including bispectrum [ 16 ], cumulant [ 17 ], wavelet transform [ 18 ], short-time Fourier transform [ 19 ], Hilbert–Huang transform [ 20 ], and Wigner–Ville transform [ 21 ]. However, these traditional SEI methods rely heavily on expert knowledge and prior knowledge [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

A Complex-Valued Self-Supervised Learning-Based Method for Specific Emitter Identification

Zhao

Yang

Liu

et al. 2022

Entropy

View full text Add to dashboard Cite

Specific emitter identification (SEI) refers to distinguishing emitters using individual features extracted from wireless signals. The current SEI methods have proven to be accurate in tackling large labeled data sets at a high signal-to-noise ratio (SNR). However, their performance declines dramatically in the presence of small samples and a significant noise environment. To address this issue, we propose a complex self-supervised learning scheme to fully exploit the unlabeled samples, comprised of a pretext task adopting the contrastive learning concept and a downstream task. In the former task, we design an optimized data augmentation method based on communication signals to serve the contrastive conception. Then, we embed a complex-valued network in the learning to improve the robustness to noise. The proposed scheme demonstrates the generality of handling the small and sufficient samples cases across a wide range from 10 to 400 being labeled in each group. The experiment also shows a promising accuracy and robustness where the recognition results increase at 10–16% from 10–15 SNR.

show abstract

Specific Emitter Identification Based on Multi-Level Sparse Representation in Automatic Identification System

Cited by 73 publications

References 27 publications

Virtual Adversarial Training-Based Semisupervised Specific Emitter Identification

Virtual Adversarial Training-Based Semisupervised Specific Emitter Identification

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

A Complex-Valued Self-Supervised Learning-Based Method for Specific Emitter Identification

Contact Info

Product

Resources

About