# Review and Simulation of Counter-UAS Sensors for Unmanned Traffic Management
## 2 Review of the State-of-the-Art Technology
### Detection through UAS Radio Frequency Signals  
UAVs usually have at least one RF communication data link to their remote controller to either receive control commands (typically at 2.4 GHz) or deliver aerial images. In this case, the spectral patterns of such transmission are used for the detection and localization of UAVs. In most cases, software-defined radio receivers are employed to intercept the RF channels. To utilize the spectrum patterns of UAVs, three possible approaches are considered for drone detection in [22]. One of them is based on sniffing the communication between drone and its controller is a clear application of this approach. Another approach is the one explained in [23], where the frequency hopping spread spectrum signals from a UAV are extracted. According to these articles, it is possible to train a classifier for identifying unique RF transmission patterns from UAVs. Data traffic patterns are also an important feature to classify and identify UAVs. In [24], a UAV's detection and identification system, using two receiver units for recording the received signal strength resulting from the UAV was proposed. The system makes use of a novel machine learning-based for efficient identification and detection of UAVs. The system consists of four classifiers working in a hierarchical way. In the first classifier, the availability of the sample as UAV is checked, while the second classifier specifies the type of the detected UAV. The third and fourth classifiers handle specific vendors' drone types. The system detects UAVs flying within the area, and it can classify UAVs and flight modes of the detected UAV with an accuracy around 99%. Another UAV detection and identification approach is based on Wi-Fi signal and radio fingerprint, as presented in [25]. Firstly, the system detects the presence of a UAV, and features from RF signal are extracted using Machine Learning and Principal Component Analysis-derived techniques to extract RF fingerprints. The extracted UAV fingerprints are \begin{table}
\begin{tabular}{c c} \hline \hline
**Specification** & **Value** \\ \hline Frequency band & X-band \\ Detection probability & 80\% \\ Detection range & 6 km \\ Detection velocity & 0.2–100 m/s \\ Elevation beamwidth & 20\({}^{\circ}\) \\ Azimuth accuracy & \(\leq\)1\({}^{\circ}\) (RMS) \\ Azimuth resolution & \(\leq\)2\({}^{\circ}\) \\ Azimuth coverage & 360\({}^{\circ}\) \\ Range accuracy & \(\leq\)5 m \\ Range resolution & \(\leq\)15 m \\ Velocity accuracy & \(\leq\)0.2 m/s \\ Scanning rate & 90\({}^{\circ}\)/s \\ Target tracks & \(>\)200, Track While Scan \\ Clutter suppression & \(\geq\)45 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Doruk radar specifications. stored and used as training data and test data. The results of this approach are above 95% in indoor scenarios and above 93% in outdoor scenarios. The real scenarios are not controlled, so it is not so easy to pick up the RF signals, as there is interference in the environment. The following two studies have carried out their experiments with interference in the radio frequency band. The proposed method in [26] relies on machine learning-based RF recognition and considers that the bandwidth of the video signal and Wi-Fi are identical. The process consists of extracting 31 features from the Wi-Fi signal and the UAV video signal and then introducing them to the classifier. It is demonstrated that the proposed method can accurately recognize UAV video signal in the presence of Wi-Fi interference. The proposed method has a recognition rate greater than 95% in the 2 km outdoor experiment. On the other hand, a radio frequency-based drone detection and identification system under wireless interference (Wi-Fi and Bluetooth), by using machine learning algorithms and a pretrained convolutional neural network-based algorithm called SqueezeNet as classifiers is explained in [27]. Different categories of wavelet transforms are used to extract features from the signals. From these extracted features, different models have been built.