# A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones
## 3 Sensing System
### Sensing Technologies
#### 3.1.1 Acoustic Sensors  
The engine and propellers of the drones generate acoustic waves in the frequency range between 20 Hz and 20 kHz, which give rise to the acoustic signature of the vehicle. A single microphone can acquire this information and thanks to the comparison with a library of acoustic signatures, it can distinguish a drone from other objects and carry out the identification phase of the aircraft by obtaining information on the model. If the number of microphones is increased, it is possible to adopt spatial diversity techniques or use beamforming techniques by arranging the installation of an array of microphones onboard the drone. In this way, it is possible to estimate the azimuth and elevation of one or more targets through the direction of arrival (DoA), perform multiple target tracking and mitigate the ego-noise effects, i.e., the noises of the electric motors and the moving propellers of the drone itself.  
This type of sensor is particularly economical, but is sensitive to environmental noise and climatic conditions related to wind or temperature and typically has a detection range that depends also on the microphone array size. This technology is typically used for ground-based counter-UAS platforms, but no airborne commercial products have been found. However, as the following articles demonstrate, the dimensions of a microphone array are compatible with the installation onboard a drone. For example, in [32] and in [33], some small-sized drones were set up with an array of microphones to locate a generic noise source. The ability to perform localization and tracking in terms of DoA and to identify noise sources were analyzed in [34], in which a circular array (ground-based) was used and, thanks to sound signal processing and array signal processing, an identification success rate of 80% was shown under the test conditions described in the article.  
The detection range depends on the quality of the microphones, the characteristics of the array and the type of processing performed. In fact, the results that can be found in the literature vary in a fairly wide range, from 5 m of [35] up to 600 m of [36]. In [37], a ground-based system of two arrays of four microphones (spaced by 1 m) each was used for the location of a drone through the calculation of the DoA. Comparable results were obtained with GPS accuracy and a detection range of 100 m. In [36], a ground-based configuration was used with the arrangement of the array of tetrahedron microphones. In this case, a detection range of up to 600 m with a success rate of 99.5% was highlighted, at the sametime, however, the tracking capabilities were poor. In [38], on the other hand, an array of 120 elements arranged on a spherical structure allowed the detection and identification of various commercial drones up to about 290 m. It should be noted that the classification range may be lower than the detection range, as shown by the test campaigns carried out for the system described in [39].  
Finally, an array of eight microphones was used in a scenario that is very similar to the one considered in this study ([35]), involving a tracker drone and an intruder drone. In this case, machine learning was used, and signal processing is done in real-time onboard the aircraft. The detection range was extremely small, equal to about 5 m, but there were excellent tracking capabilities.