Classification of Small UAVs Based on Auxiliary Classifier Wasserstein GANs

Beyond their benign uses, the small Unmanned Aerial Vehicles (UAVs) are expected to take the major role in future smart cities that have attracted the attention of the public and authorities. Therefore, detecting, tracking and classifying the type of UAVs is important for surveillance and air traffic management applications. Existing UAVs detection works focus on radars, visual detection, and acoustic sensors. However, the work was done by applying Support Vector Machine (SVM), k-Nearest Neighbor (KNN) based methods to classify the UAVs need a large number of samples for feature extraction to train a model. In this paper, we propose a new small UAVs classification system using Auxiliary Classifier Wasserstein Generative Adversarial Networks (AC-WGANs) based on the wireless signals collected from the UAVs of various types. Before the classification, using the Universal Software Radio Peripheral (USRP), oscilloscope and antenna to collect the wireless signals, preprocessing and dimensionality reduction to represent information at a lower dimension space. The processed data from UAVs is input to the UAVs' discriminant model of the AC-WGANs for classification. The obtained results show the effectiveness of the proposed system, which can achieve a recognition accuracy of around 95% in the indoor environment and can also be suitable in the outdoor environment.