# Drone and Controller Detection and Localization: Trends and Challenges
## 4 Drone Controller Detection and Localization  
Once UAVs are detected, the detection and localization of the drone controller are implemented to monitor their communication and limit illegal use. UAV detection systems differ according to the technologies and functions performed, such as identification, classification, tracking, localization, interdiction, destruction, and damage. Technology and functions are selected and implemented based on the main requirements of the UAV detection system. In this section, some detection systems that support localization functionality are reviewed. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Detection Technique** & **Summary** & **Limitations** & **Ref.** \\ \hline \multirow{6}{*}{Radio Frequency} & Real-time analysis for the detected radio communication between UAV and its controller. However, it does not apply to autonomous UAV detection. & & \\  & Low cost and simple architecture and elements: Antennas, Processors, RF sensors. Power and sensitivity of each affect detection system performance and accuracy. & The RF-based detection technique applies only if the UAV is remotely controlled. & [7; 8; 9; 10] \\  & Common frequency bands are around 2.4 and 5 GHz Covering a long detection range will perform more efficiently in the less congested RF zones. & Referring to RF datasets and integrating with machine learning algorithms are advanced ways to enhance detection, localization, and precise classification. & [7; 8; 9; 10] \\ \hline \multirow{6}{*}{Radar} & Transmitting radio signals, then receiving and analyzing the reflection/backscattering/echo radar signals. & UAVs generally have limited Radar Cross Sections similar to birds or pedestrians. The amount of false positives remains high and low-RCS limits the radar, especially X-band Radars. & UAVs generally have limited Radar Cross Sections similar to birds or pedestrians. The amount of false positives remains high and low-RCS limits the detection range of the radar, especially X-band Radars. & [21; 22; 23; 24; 25; 26; 27; 28; 29] \\  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \multirow{6}{*}{Acoustic} & Analyze acoustic signals coming from UAV’s engine or propeller blades. & & \\ \cline{1-1}  & Acoustic sensors/microphones arrays combined with data acquisition and signal processing modules & & \\ \cline{1-1}  & Acoustic fingerprint analysis, features extraction, classification, and localization & & \\ \cline{1-1}  & UAV’s identification and distinction from other objects & & \\ \cline{1-1}  & Effective in a short distance, however, it’s affected by the nearby noise sources and weather. & & \\ \cline{1-1}  & Acoustic dataset and Machine learning techniques & & \\ \cline{1-1}  & integration for higher performance (detection and classification). & & \\ \hline \multirow{6}{*}{Electro-optic} & Imaging and motion line of sight detection. & & \\ \cline{1-1}  & High-cost equipment & & \\ \cline{1-1}  & Ability to track autonomous UAVs. & & \\ \cline{1-1}  & Controlling false alarms with advanced integration with other methods/algorithms/machine learning. & & \\ \cline{1-1}  & Detection performance can vary with different environmental conditions and weather. & & \\ \cline{1-1} \cline{1-1}  & & & \\ \cline{1-1} \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of all reviewed UAV detection techniques. The process of locating and positioning a UAV is mainly based on collecting the direct measurements of the detected UAV and its emitted signals. These direction measurements and other extracted features are calculated and utilized in the UAV detection system to estimate the geolocation of the UAV.