Unsupervised specific emitter identification of bispectrum features based on contrastive learning

Li Pengcheng; Yingke Lei; Li Haitao; Zhou Yu

doi:10.1117/12.2684988

19 October 2023 Unsupervised specific emitter identification of bispectrum features based on contrastive learning

Li Pengcheng, Yingke Lei, Li Haitao, Zhou Yu

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 1270919 (2023) https://doi.org/10.1117/12.2684988
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

Aiming at the problem that features of communication emitter data without label information are different to extract and the classification acquisition is not high, this paper cites the contrastive learning theory, constructs a residual network with two parameters sharing as the backbone network, conducts contrastive learning on the rectangular integral bispectral features of signal augmented samples, and further extracts the feature presentation with more differentiation. To this end, the feature separability between samples from different emitters is enhanced. Then, the new features extracted are used for contrastive learning at the cluster level to complete the tasks of classification and identification. Compared with other unsupervised learning algorithms, the proposed method achieves a better identification accuracy of about 78% through experiences on the dataset of measured ultra-short wave communication stations.

Citation Download Citation

Li Pengcheng, Yingke Lei, Li Haitao, and Zhou Yu "Unsupervised specific emitter identification of bispectrum features based on contrastive learning", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 1270919 (19 October 2023); https://doi.org/10.1117/12.2684988

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KEYWORDS

Feature extraction

Data communications

Data modeling

Education and training

Data acquisition

Matrices

Neural networks

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