# Drone and Controller Detection and Localization: Trends and Challenges
## 3 UAV Detection Methods
### Electro-Optical  
The electro-optic sensing system transmits, detects, and examines radiations in the optical spectrum, including visible light, infrared, and ultraviolet radiation. It can handle long-range imaging and has reliable results under different illumination levels. The components associated include optics, laser, detectors, camera, processing unit, etc. Such systems have been used for UAV detection, direction finding, and localization continuously and in all weather conditions. In [46], the authors proposed using machine learning techniques to automatically detect and track small moving objects in the airfield from their motion patterns, i.e., the ways an object moves. The system utilized remote digital towers with high-resolution cameras covering the 360-degree view of airports to construct a video dataset comprising aircraft in an airfield and drones. Harris detection and convolutional neural network followed by optical flow we applied to the dataset to locate and track very small moving objects in the wide-area scene. The results showed that the system can detect objects with \(15\times 15\) pixels in 1080p images with a low miss rate. Motion-based features are extracted from their trajectories, after which a K-nearest neighbor classifier is applied to classify objects into drones or aircraft, with an accuracy of 93%. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Detection Technique**} & \multicolumn{5}{c}{**Functionalities**} \\ \cline{3-6}  & & **Identification** & **Classification** & **Localization/ Tracking** & **Performance** \\ \hline \multirow{4}{*}{[30]} & Designed for Amateur & & & & \\  & Drones (200 Hz), SVM & \(\surd\) & - & \(\surd\) & High accuracy \\  & (Drone sound & & & & \\  & identification) & & & & \\ \cline{1-1}  & BiLSTM (UAV sound & \(\surd\) & \(\surd\) & - & UAV sounds 94.02\% \\  & classification) & \(\surd\) & - & - & 96.3\% \\ \cline{1-1}  & Concurrent Neural & & - & - & 96.3\% \\ \cline{1-1}  & Networks & \(\surd\) & - & \(\surd\) & SRP-PHAT outperform \\ \cline{1-1}  & TDoA & SRP-PHAT & \(\surd\) & - & \(\surd\) & TDoA \\ \cline{1-1}  & - & \(\surd\) & - & \(\surd\) & - \\ \cline{1-1}  & MFCC, STFT, CNN, SVM & \(\surd\) & - & \(\surd\) & Noise affects the detection \\ \cline{1-1}  & SRP-PHAT & \(\surd\) & - & \(\surd\) & \(\surd\) \\ \cline{1-1}  & SRP-PHAT & \(\surd\) & - & \(\surd\) & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of reviewed acoustic-based techniques for UAV characterization. The proposed approach in [42] performs the detection by integrating a 3D LADAR sensing system. The study employed voxel-based background subtraction and variable radially bounded nearest neighbor (V-RBNN) techniques to detect small UAVs up to 2 km. During the development phase, this integration is supported with augmented data set to enhance the model's performance. The developed LADAR scanner can be rotated to cover a wide range of areas, e.g., 350 degrees for azimuth direction and 120 degrees for elevation direction. Furthermore, the used clustering algorithm, V-RBNN, has a good impact on the target UAV classification, which may increase the use of this proposed detection system in various applications. In some electro-optic solutions, the detected data transferred to the analysis phase, including advanced processing, machine vision, or machine learning, are not accurate enough to track small UAVs effectively. Authors in [44] proposed an electro-optic system integrated with an all-sky camera system to get a wider view of the monitored area to improve the detection resolution. Multiple experiments were performed to evaluate the proposed solution and test its integration with other cues, i.e., acoustic. The combination of these three systems, electro-optic, all-sky camera, and acoustic cues, is also evaluated. The study in [41] improved its outcomes' reliability by using the electro-optical method for small UAV detection and tracking.