Machine Learning-Based Jamming Classification Scheme for Real GPS L1 C/A Signal 


Vol. 46,  No. 11, pp. 1804-1806, Nov.  2021
10.7840/kics.2021.46.11.1804


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  Abstract

In this paper, we propose a CNN (Convolution Neural Network)-based jamming signal classification scheme, which is a representative machine learning model that can effectively classify real GPS L1 C/A (Coarse/Acquisition) signal and five jamming signals generated in real time with our own experimental equipment. As a result of the experiment, the highest average classification accuracy of the conventional scheme is 95.29%, while the proposed scheme is 98.23%, showing a 2.94%P higher classification accuracy than the conventional scheme. In addition, the conventional scheme has a maximum difference of 9.66%P in classification performance for each jamming signal, whereas the proposed method is a more robust jamming classification scheme with a maximum of 3.03%P.

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  Cite this article

[IEEE Style]

S. Yoo, C. S. Sin, S. Y. Kim, "Machine Learning-Based Jamming Classification Scheme for Real GPS L1 C/A Signal," The Journal of Korean Institute of Communications and Information Sciences, vol. 46, no. 11, pp. 1804-1806, 2021. DOI: 10.7840/kics.2021.46.11.1804.

[ACM Style]

Seungsoo Yoo, Cheon Sig Sin, and Sun Yong Kim. 2021. Machine Learning-Based Jamming Classification Scheme for Real GPS L1 C/A Signal. The Journal of Korean Institute of Communications and Information Sciences, 46, 11, (2021), 1804-1806. DOI: 10.7840/kics.2021.46.11.1804.

[KICS Style]

Seungsoo Yoo, Cheon Sig Sin, Sun Yong Kim, "Machine Learning-Based Jamming Classification Scheme for Real GPS L1 C/A Signal," The Journal of Korean Institute of Communications and Information Sciences, vol. 46, no. 11, pp. 1804-1806, 11. 2021. (https://doi.org/10.7840/kics.2021.46.11.1804)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880