# A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones
## 3 Sensing System
### Sensing Technologies Comparison  
As seen, the proximal sensing capability of a team of mini drones is the main clear advantage over a static, ground-based CUS. For example, the optical occlusion problem could have a minor impact on a drone-based video sensing system thanks to the possibility to change the perspective, taking advantage of the mobility of the drone itself. On the other hand, there is the need for an accurate video stabilization to mitigate the blur effect due to the drone movement. In Table 2, some pros and cons of using sensing technologies integrated in mini-drone-based platforms with respect to the static ground case are summed up. Each technology has a different detection range, classification capacity and energy requirement. For example, the optical sensors in the visible work very well only in line of sight (LOS) conditions, while the RF sensors can work in non-line of sight (NLOS) conditions. It is, therefore, impossible to reach a satisfactory situational awareness level with the adoption of a single technology and, in this regard, the simultaneous adoption of different sensing techniques is the winning way, as previously addressed in the concept of the defensive team and the related cooperative sensor network. Using sensor or data fusion algorithms allows for better results than those that would be obtained individually. For example, in [69], test campaigns were carried out on a detection system that showed how the use of the data fusion technique increased the detection rate. These improvements were achieved at the expense of system complexity and computational effort. The identification of the technologies that best complement each other is a useful activity in order to optimize the level of situational awareness with respect to the complexity and cost of the system. The following tables have been constructed to better highlight the characteristics of each technology. Table 3 contains a rough estimate of the performance in terms of \"detection\", \"classification\" and \"global features characterization\". It is not easy to establish the performance of each technology in absolute terms, which is why \"low\" to \"high\" range values have been indicated and express a qualitative judgment based on the literature reported in the previous paragraphs. It should be noted that the distances detected in the experimental setups can be numerically very different from the datasheet of the CUS products that can be found on the market. The explanation of this deviation could depend on the different level of optimization that an engineering product has in front of the experimental setup and the different requirements of ground- and sky-based devices. However, the relationship between the different technologies should be respected beyond the absolute numerical values. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensing Technique** & **Pros** & **Cons** \\ \hline Acoustic & Possibility to move close to the target and improve the identification task. & Need for proper ego-noise cancellation due to the propellers noise. \\ \hline Optical & Possibility to change the perspective and to operate close to the target with a higher resolution and better identification capabilities. & Limited computational power; need for efficient video stabilization. \\ \hline RF & Better conditions of the air-to-air channel with respect to the ground-to-air one. & \\ \hline LiDAR & Possibility to move close to the target and improve the detection phase. & Limited on-board power. \\ \hline Radar & Thanks to the proximal sensing, less power of the active sensor is required. & Limited on-board power. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Pros and cons of using sensing technologies on-board a drone.