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Open AccessArticle

Application of Auxiliary Classifier Wasserstein Generative Adversarial Networks in Wireless Signal Classification of Illegal Unmanned Aerial Vehicles

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Department of Communication Engineering, Xiamen University, Xiamen 361005, China
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Department of Communication Engineering, Tibet University, Lhasa 850000, China
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Department of Electrical Engineering, Wright State University, Dayton, OH 45435, USA
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(12), 2664; https://doi.org/10.3390/app8122664
Received: 13 November 2018 / Revised: 5 December 2018 / Accepted: 10 December 2018 / Published: 18 December 2018
(This article belongs to the Special Issue UAV Assisted Wireless Communications)
Recently, many studies have reported on image synthesis based on Generative Adversarial Networks (GAN). However, the use of GAN does not provide much attention on the signal classification problem. In the context of using wireless signals to classify illegal Unmanned Aerial Vehicles (UAVs), this paper explores the feasibility of using GAN to improve the training datasets and obtain a better classification model, thereby improving the accuracy of classification. First, we use the generative model of GAN to generate a large datasets, which does not need manual annotation. At the same time, the discriminative model of GAN is improved to classify the types of signals based on the loss function of the discriminative model. Finally, this model can be used to the outdoor environment and obtain a real-time illegal UAVs signal classification system. Our experiments confirmed that the improvements on the Auxiliary Classifier Generative Adversarial Networks (AC-GANs) by limited datasets achieve excellent results. The recognition rate can reach more than 95% in the indoor environment, and this method is also applicable in the outdoor environment. Moreover, based on the theory of Wasserstein GANs (WGAN) and AC-GANs, a more robust Auxiliary Classifier Wasserstein GANs (AC-WGANs) model is obtained, which is suitable for multi-class UAVs. Through the combination of AC-WGANs and Universal Software Radio Peripheral (USRP) B210 software defined radio (SDR) platform, a real-time UAVs signal classification system is also implemented. View Full-Text
Keywords: GAN; AC-WGANs; wireless signals; classify model; USRP GAN; AC-WGANs; wireless signals; classify model; USRP
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Zhao, C.; Chen, C.; He, Z.; Wu, Z. Application of Auxiliary Classifier Wasserstein Generative Adversarial Networks in Wireless Signal Classification of Illegal Unmanned Aerial Vehicles. Appl. Sci. 2018, 8, 2664.

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