Classification and identification of communication jamming signals of intelligent connected vehicle based on CNN

Chuang Hu; Muxi Li; Xuezhu Yang; Houli Chen

doi:10.1117/12.2668049

20 April 2023 Classification and identification of communication jamming signals of intelligent connected vehicle based on CNN

Chuang Hu, Muxi Li, Xuezhu Yang, Houli Chen

Author Affiliations +

Proceedings Volume 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022); 126023J (2023) https://doi.org/10.1117/12.2668049
Event: International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 2022, Changchun, China

Abstract

With the development of intelligent connected vehicles, especially L3 level automatic driving technology, it is very important to identify and avoid the impact of malicious jamming signals on the communication of intelligent connected vehicles. In this paper, we classify and identify the five kinds of common intelligent connected vehicle communication jamming signals: single-tone jamming signal, multi-tone jamming signal, linear swept-frequency jamming signal, partial band jamming signal and noise frequency modulation jamming signal. Firstly, six kinds of features of communication jamming signals are extracted: frequency domain moment kurtosis coefficient, frequency domain moment skewness coefficient, single frequency encircled energy, average spectral flatness coefficient, frequency domain parameter and time domain moment kurtosis coefficient, and then classified and identified based on convolution neural network (CNN). The simulation results show that when the jamming-to-noise ratio (JNR) is higher than -7dB, the recognition rate is over 92%, which indicates that CNN has good classification and recognition performance for communication jamming signals.

Citation Download Citation

Chuang Hu, Muxi Li, Xuezhu Yang, and Houli Chen "Classification and identification of communication jamming signals of intelligent connected vehicle based on CNN", Proc. SPIE 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 126023J (20 April 2023); https://doi.org/10.1117/12.2668049

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