# Drone and Controller Detection and Localization: Trends and Challenges
## 3 UAV Detection Methods
### Hybrid Fusion Systems  
The hybrid fusion of multiple cues, such as radio frequency, radar, acoustic, and visual sensors, improves the performance of detection, classification, and localization of both the drone and its controller. [PERSON] et al. [47] suggested an array of sensors to collect the detected drone's data to be fed to a fusion engine for further analysis using the multiple hypothesis tracker (MHT) techniques, as illustrated in Figure 7. The RF signal is received and processed to compute the detected drone's DOA for drone localization. The captured  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Detection Technique**} & \multicolumn{4}{c}{**Functionalities**} \\ \cline{3-6}  & & **Identification** & **Classification** & **Localization/ Tracking** & **Performance** \\ \hline
[42] & 3D LADAR sensor, 3D & & & & \\  & background subtraction, & \(\surd\) & - & \(\surd\) & Detection Range 2 km \\  & V-RBNN & & & & \\
[44] & Combination of: EO/IR, & \(\surd\) & - & \(\surd\) & Line of sight limitation \\
[41] & All-sky, and acoustic cues & \(\surd\) & - & \(\surd\) & - \\
[41] & Real stream detection, & \(\surd\) & - & \(\surd\) & - \\  & Differential method & & & & Accurate Detection range \\
[45] & DVS camera, Temporal & \(\surd\) & - & \(\surd\) & 30 m \\
[43] & Background subtraction, & & & & Moving Background \\  & CNN’s & \(\surd\) & - & - & dependency \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of reviewed electro-optical sensor-based techniques for UAV characterization.  
Figure 6: Proposed UAV detection algorithm used in electro-optical method [43].  
signals from the integrated acoustic sensors are filtered to remove unwanted noise. After identifying and detecting the drone signatures, the coherent broadband beamforming technique is used to recognize the drone bearing angle and reduce its error by the two-step filter. Finally, the extracted DOA and bearing angle are referred to by the fusion engine for localization purposes. Finally, the GSM passive radar [41] is used for UAV detection and localization, and its output is fed to the fusion engine of the overall system. Combining all these technologies improved the system performance and enhanced the localization accuracy.  
In [40], the proposed UAV detection and localization system relies on time delay and beamforming of the collected acoustic signal from a set of microphones. Acoustic signal characteristics with such signal processing are used to find the DOA of the UAV's detected recorded signal. Furthermore, Kalman filtering is used to improve the UAV's trajectory. The system is designed to identify and track the RF signal emitted by portable RF devices [48]. The system consists of two parts: (1) RF signal acquisitions achieved by an antenna array followed by a Nyquist ADC converter and (2) signal processing. The RF signal from the first part is passed into FFT to measure the DOA. The DOA is passed into the digital bandpass filter to measure the TDOA, which is used together with the DOA to estimate the location. AOA calculated from the DOA, the location, and past tracking information are used for tracking the drone's position [40].