## 1 Introduction
[PERSON] [24] provided a detection method based on Medium Access Control (MAC) address given the fact that many low-end commercial UAVs have identifiable service set identifier (SSIDs) and MAC address broadcasting. However, this MAC address based detection method is vulnerable to interference, and wireless protocols must be known beforehand. Figure 1: The application UAVs classification systems in the scene of multiple UAVs. [PERSON] et al. [25] proposed a detection algorithm based on an Artificial Neural Network (ANN) where the recognition rate is greater than 82% within a distance of 3 km. [PERSON] et al. [26] presented an SDR-based, portable universal software radio peripheral (USRP) system for detection in two scenarios. For the scenario in which a UAV communicates with the ground controller, the cyclostationarity signature of the drone signal and pseudo-Doppler principle are employed. For the scenario in which a UAV is not sending any signal, a micro-Doppler signature generated by the radio frequency signal is exploited for detection and identification. [PERSON] et al. [27] proposed a WiFi-based approach that aimed at detecting nearby aerial or terrestrial devices by performing statistical fingerprint analysis on wireless traffic. However, most of the above-mentioned methods are not very robust and cannot detect illegal UAVs. Due to the small size, low flying height and slow speed of most UAVs, most of existing detection systems do not perform well. Small UAVs are not easy to distinguish from birds and can be hidden by urban buildings, therefore the detection technology has a high false positive rate. In this paper, an SDR-based wireless signal detection technology is used to collect the wireless communication signals of the UAVs, and is then combined with an Auxiliary Classifier Wasserstein Generative Adversarial Networks (AC-WGANs) classification model to realize low-cost, high-precision UAV detection and recognition. Due to the crystal oscillation of the device, component jointing, and the loss of electronic device, the radio frequency (RF) signals of the device are slightly different, and thus can be distinguished. The technology based on wireless signals detection has been used to classify a number of Internet-of-Things (IoT) devices (same model and same RF front-end), which proves the feasibility of this method to detect the UAVs [28]. The technology based on wireless signal detection of UAVs has two main challenges. On the one hand, a variety of wireless signals exist in the real environment. As a direct result, it is very difficult to accurately collect the UAVs signals, label the signals, and establish a reliable database. On the other hand, the classification models such as k-Nearest Neighbor (KNN), Convolutional Neural Networks (CNN), and Support Vector Machine (SVM) need to select the appropriate feature signals for classification through feature extraction. In the process of feature selection, important feature information is often lost, and the recognition rate of the signal is seriously affected. To solve these two challenges, we use the combination of oscilloscope and USRP to acquire the signals of UAVs in the indoor environment and the outdoor environment to establish the database. Next, we propose an automatic classification model of the UAV based on AC-WGANs. Without special feature extraction, only a modified principal Component Analysis (PCA) reduction operation is needed to realize the automatic classification of the UAVs signals. The contributions of this paper are as follows: