## 4 Experiments
### Indoor Environment  
In the indoor environment, the situation of wireless signals is very complicated, especially because the wireless signals in the 2.4-2.5 GHz frequency range also include IEEE 802.11b and IEEE 802.11n WiFi signals. Thus, we first analyzed the wireless signals ranging from 2.4 to 2.5 GHz. We used an Agilent (DSO9404A) oscilloscope to collect the wireless signal of different devices. The oscilloscope has a sampling bandwidth of 1 GHz and a sampling rate of 20 GSa/s, which meets the sampling requirements [40]. It can be found that, even if the same WiFi protocol is used, different mobile phone models and different devices, such as routers, have different features. Figure 6a shows the signal acquisition process of the oscilloscope. The collected signal was detected to obtain the processed start-point signal. Figure 6b shows the signal waveforms of different devices. It can be found that different devices have different waveforms generated by different WiFi protocols.  
Most of the UAV and WiFi signals are in the frequency range of 2.4-2.5 GHz, and the preamble of the wireless signal is identifiable and can be used for classification detection of the UAVs [41]. We selected five types of UAV signals from different manufacturers for detection and identification: Phantom, Hubsan, Mi, WiFi, and Xiro. Bandpass filtering, start-point detection [42] and dimensionality reduction were performed. After that, the model was trained and identified in the AC-WGANs model. The AC-WGANs algorithm flow includes four steps: signal acquisition, signal preprocessing, model training, and classification (Figure 7). The signal acquisition process of the UAV will cause some low-frequency noise due to factors such as environment and equipment stability. Therefore, the noise was filtered by setting a bandpass filter in the 2.4 GHz band, and then the start-point detection was performed. Finally, extracting the envelope data and reducing the dimensionality of the wireless signals were performed..  
Figure 5: The application of the UAV classification system.  
After the pre-processing of the wireless signals, the AC-WGANs classify model was used for classification. The UAVs signal and WiFi signal was input to this model and the probability of each label was compared to determine the type of the inputting signal. To analyze the influence of noise on model recognition rate, artificial noises with a signal-to-noise ratio (SNRs) of 5 dB to 35 dB were added in the wireless signals to compare the effects of different noises on model recognition rate. Figure 8 shows the classification rate of wireless signals in different SNRs. In the case of low SNRs, the classification rate of the wireless signals is higher than the rate of the wireless signals.  
Figure 6: Detecting the application scene of the UAVs.  
Figure 7: The pre-processing of the wireless signals.  
especially at 5 dB, the recognition rate was significantly improved, reaching more than 95%, and the signal may be classified as an unknown signal if the signal isn't in the datasets of the classify model, as shown in Table 3. Figure 7(a) shows the classification of AC-WGANs based on PCA and Restricted Boltzmann Machine (RBMs) [43]. In addition to the PCA, the RBMs was used in the dimensionality reduction, and also had good performance. However, considering the time complexity and space complexity, the PCA is more suitable. Figure 7(b) shows the comparison of the recognition rates of various algorithms, including Deep Belief Nets (DBN) [44], KNN, SVM, and GANs. Considering the stability and recognition rate, it can be seen that the improved AC-WGANs is better.