# Drone and Controller Detection and Localization: Trends and Challenges
## 3 UAV Detection Methods
### Radar  
Radar signal processing is among the classical approach for aircraft and drone detection as it can be used in all weather conditions with 24/7 operation [18; 21; 52] as compared to acoustic and visual detection methods. In this approach, the received signal is characterized to detect echo, doppler signature, or radar cross-section (RCS) for detecting and tracking the target [21; 28; 29; 53]. The conventional radar signal processing techniques have the limitation of accurate distinction of mini UAVs from birds due to their smaller RCSs. AI-based techniques are proposed [28; 29; 53; 54] to process the extracted features from the radar signals to address this issue to some extent. In radar-based detection, radio energy is used to detect the target and define its position [21; 23; 55]. Typically, a radar-based detection system has three main components: RF radar, data acquisition, and signal processing. In RF radar, the electromagnetic energy radiates into space and encounters the UAV's body flying in the monitored area. The UAV's reflected wave is returned and received by the system, measured, and processed in real-time (data acquisition and signal processing). Hence, the UAV is successfully located, and its flight path is tracked by the system [30; 36; 55; 56]. Frequency-modulated continuous wave (FMCW) and continuous wave (CW) radars are preferred to be used in UAV detection and identification, especially for their continuous pulsing, effective cost, and performance [21]. The FMCW radar contains a transmitter and a receiver antenna. The oscillator and the control signal produce the transmitted signal. After the backscattering/reflected signal is received, it gets passed to the I/Q demodulator for filtering. Power is equally distributed into two signals with 90 degrees phase shift to be forwarded to the low pass filter (LPF). The intermediate frequency (IF) signal, resulting from in-phase and quadrature-phase components, is directed to the analog-to-digital converter (ADC) and the digital signal processing (DSP), as depicted in Figure 3. The distance and velocity of the target can be defined by using the time delay and phase information of both the transmitted and received signals [21]. The studies of [24; 25; 28; 54; 57; 58] employed the principal component analysis (PCA) [24], convolutional neural networks (CNN) [23; 28; 51; 54], long short-term memory (LSTM) [28], and support vector machines (SVM) [57; 58] techniques for the processing of extracted features from radar signals such as micro-doppler spectrogram [23; 28; 54; 57; 58] and range-doppler signature [24] for the classification of drones. Recently authors in [13] used the hierarchical learning approach for the detection of the presence, type, and flight trajectory of a UAV. Due to the smaller size of most UAVs, wideband, high frequency, expensive radars are required for the accurate detection and tracking of mini UAVs [23; 24; 52; 54], which increases the overall cost of the detection and localization system. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Operating Frequency**} & \multicolumn{4}{c}{**Functionalities**} \\ \cline{3-6}  & & **Identification** & **Classification** & **Localization/ Tracking** & **Performance** \\ \hline
[7] & 2.4 GHz & \(\surd\) & \(\surd\) & - & Average of 97\% \\
[8] & 2.4 GHz & \(\surd\) & \(\surd\) & - & Greater than 96\% \\
[9] & 2.4 GHz & \(\surd\) & \(\surd\) & - & Average of 96.3\% \\
[11] & 20 MHz–6 GHz & \(\surd\) & - & \(\surd\) & - \\
[13] & 1–6 GHz & \(\surd\) & \(\surd\) & - & Average of 99\% \\
[10] & 2.401–2.481 GHz & \(\surd\) & \(\surd\) & \(\surd\) & - \\
[16] & 2.4 GHz and 5.8 GHz & \(\surd\) & - & - & - \\ ISM bands & \(\surd\) & - & - & - & - \\
[12] & 2.4 GHz & \(\surd\) & - & \(\surd\) & - \\
[18] & 2.4 GHz ISM band & \(\surd\) & - & \(\surd\) & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of reviewed RF-based techniques for UAV characterization.