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

Abstract

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.

Transfer Learning-Based Specific Emitter Identification for ADS-B over Satellite System

“…In [20], specific features were extracted and recognized in the Hilbert-Huang transform domain, and a support vector machine was utilized as the classifier to address the limitations of traditional methods, which relied on expertise and struggled to adapt to waveform variations. A feature extraction method based on bispectrum transform (BST) was proposed in [21][22][23][24]. This method achieves high recognition accuracy and performs well in noise suppression.…”

Specific emitter individual identification based on the characteristics of synchronization signal rising edges and deep neural network

“…Now China's technology in the SEI field is developing rapidly and gradually becoming practical. More and more Chinese researchers in universities and institutions have achieved excellent work in SEI research, such as XiaoniuYang [3][4][5] , Yingke Lei 6,7 , Junan Yang 8 , Zhitao Huang [9][10][11] , Limin Zhang [12][13][14] , Guoru Ding 15 , Fanggang Wang [16][17][18] .…”