Abstract
Nowadays, in the complex electromagnetic environment, the detection of foreign satellite, the electronic interferences and the sensing data tampering in the process of consistent spectrum situation fusion and the electronic countermeasures reconnaissance and enforcement implemented by the enemy electronic attacks all pose serious threats to the communication performance of our electronic devices and communication systems. Therefore, how to detect these electromagnetic spectrum threats effectively is very important. The generative adversarial networks was applied in this paper, which is a method in deep learning, and an unsupervised solution for the above-mentioned electromagnetic spectrum threat signal prediction problem was provided, which has achieved good results. To carry out the detection experiments, three common electromagnetic spectrum threat scenarios were simulated. The prediction performance of the model is evaluated based on the prediction accuracy of the model. The experimental results have shown that the generative adversarial networks model used in this paper has a good predictive effect on the electromagnetic spectrum threat signals of a certain intensity.
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Acknowledgment
This work is supported by the National Natural Science Foundation of China (61771154) and the Fundamental Research Funds for the Central Universities (HEUCFG201830). This paper is also funded by the International Exchange Program of Harbin Engineering University for Innovation-oriented Talents Cultivation. Meantime, all the authors declare that there is no conflict of interests regarding the publication of this article. We gratefully thank of very useful discussions of reviewers.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wei, C., Qi, L., Wu, R., Lin, Y. (2019). Electromagnetic Spectrum Threat Prediction via Deep Learning. In: Liu, S., Yang, G. (eds) Advanced Hybrid Information Processing. ADHIP 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 279. Springer, Cham. https://doi.org/10.1007/978-3-030-19086-6_48
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DOI: https://doi.org/10.1007/978-3-030-19086-6_48
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