Communication transmitter individual identification based on GAF-ResNet

Yao Yu; Jiantao Zhang; Dongtao Li; Zhifang Feng

doi:10.1117/12.2635911

5 May 2022 Communication transmitter individual identification based on GAF-ResNet

Yao Yu, Jiantao Zhang, Dongtao Li, Zhifang Feng

Proceedings Volume 12245, International Conference on Cryptography, Network Security, and Communication Technology (CNSCT 2022); 122450G (2022) https://doi.org/10.1117/12.2635911
Event: International Conference on Cryptography, Network Security, and Communication Technology (CNSCT 2022), 2022, Sanya, China

Abstract

In order to solve the problem that the traditional method of communication transmitter individual identification ignores the nonlinearity, shallow features and timing characteristics of the signal, this paper proposes a method of communication transmitter individual identification based on GAF-ResNet. This method uses the Gramian Angular Field to convert timeseries signals into two-dimensional images of time-domain signals, which takes better advantages of the dependence and correlation of time series. The residual block of the residual network is used to realize the fusion between shallow features and deep features. The experimental results show that the best network model trained by this method has a recognition rate of 92.7%, which proves the effectiveness of the method.

Citation Download Citation

Yao Yu, Jiantao Zhang, Dongtao Li, and Zhifang Feng "Communication transmitter individual identification based on GAF-ResNet", Proc. SPIE 12245, International Conference on Cryptography, Network Security, and Communication Technology (CNSCT 2022), 122450G (5 May 2022); https://doi.org/10.1117/12.2635911

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KEYWORDS

Signal processing

Time-frequency analysis

Feature extraction

Data modeling

Image processing

Transmitters

Fourier transforms

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