# Review and Simulation of Counter-UAS Sensors for Unmanned Traffic Management
## 2 Review of the State-of-the-Art Technology
### Detection by Acoustic Signals  
An array of acoustic sensors can be employed to capture the sound, detect, and estimate the direction of arrival of sounds from sources such as UAVs. These arrays are deployed around the restricted areas and record the audio signal periodically and deliver this signal to the ground stations. The ground stations extract the features of this audio signal to determine the direction of arrival of the UAV. Conventionally, once the audio signal of UAV is received, the power or frequency spectrum is analyzed to identify the UAV. An example implementation of this type of UAV detection is explained in [31]. This paper shows how to estimate and track the location of a target by triangularization with two or more microphone arrays, in addition to how the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Specification** & **DedroneSensor RF-160** & **DedroneSensor RF-360** \\ \hline Range & 1.6–5 km (depending on RF interference conditions) & 2–5 km (depending on RF interference conditions) \\ Radio frequency & Omnidirectional, passive detection and classification & Omnidirectional, passive detection, classification and direction-finding \\ \hline \hline \end{tabular}
\end{table} 
Table 7: DedroneSensor’s RF sensors’ specifications. UAV model can be obtained by measuring the sound spectrum of the target. In this report, a small tetrahedral array of microphones was used. The results show that the detection algorithm performs best with a 99.5% probability of detection and a 3% false alarm rate. On the other hand, the tracking algorithm often misses trajectories when other trajectories are present, and the elevation tracking is poor. Another example of UAV detection using acoustic signals is shown in [32]. In this work, the data collection equipment is composed of two individual microphone arrays in 16-X and 4-L configurations where the microphones are placed on the ground and mounted on metal spikes, while the elevated sensors are placed on tripods. These microphones are covered by six-inch-thick foam shields to protect them and limit the effects of wind. Once the signals have been captured by the arrays, they must be processed and analyzed. The data processing developed, as well as the analysis of the acoustic sensor arrays, has been tested by being used to detect and track the trajectory of UAVs at low altitude and tactical distances. This process operates best under benign daytime conditions and is approximately five times better at detecting noisier, medium-sized, gasoline-powered UAVs than small, electric-powered UAVs. In the literature, there are some machine learning (ML) approaches to classify the UAV from audio data. Support vector machine (SVM) is implemented to analyze the signal of an UAV engine and to build the signal fingerprint of UAV. The results show that the classifier can precisely distinguish the UAVs in some scenarios [33]. Another example of using deep learning methods to detect UAVs with acoustic signals is shown in [34]. In this paper, there is a comparison among Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Convolutional Recurrent Neural Networks (CRNNs) using melspectrogram features. Here, the CNNs show the better performing results, achieving the highest average accuracy of 94.7%. In summary, machine learning presents an ability to recognize and locate the UAV. However, the nature of acoustic approaches limits the deployment and detection of UAV. In [35], a detailed study was conducted on how drone detection is performed by using acoustic signals, and it characterized how the microphone array in charge of capturing the sound signal should be organized. The geometry of the microphone array depends on the application to be carried out, although, when the desired signal can come from any angle, the best geometry is the circular array. The possible geometries studied were uniform linear array (ULA), uniform circular array (UCA) and uniform rectangular array (URA).