# Drone and Controller Detection and Localization: Trends and Challenges
## 3 UAV Detection Methods
### Acoustic
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{
\begin{tabular}{c} **Detection** \\ **Technique** \\ \end{tabular} } & \multirow{2}{*}{**Specifications**} & \multicolumn{4}{c}{**Functionalities**} \\ \cline{3-8}  & & & **Identification** & **Classification** & **Localization/** **Tracking** & **Performance** \\ \hline
[21] & FMCW/CW radar &
\begin{tabular}{c} Doppler effect \\ Principle \\ C and X frequency \\ bands, Micro \\ Doppler principle \\ Operating \\ frequency: 35 GHz \\ \end{tabular} & \(\surd\) & - & - & NA \\
[55] & CW radar &
\begin{tabular}{c} Doppler effect \\ bands, Micro \\ Doppler principle \\ Operating \\ frequency: 35 GHz \\ \end{tabular} & \(\surd\) & \(\surd\) & - & - \\
[56] & CW radar &
\begin{tabular}{c} Operating \\ frequency: 35 GHz \\ \end{tabular} & \(\surd\) & \(\surd\) & - & Accuracy 85\% \\  & & & & & & Performed well \\
[27] & \begin{tabular}{c} Cylindrical phased \\ array radar \\ \end{tabular} & \
\begin{tabular}{c} Operating \\ frequency: C band \\ \end{tabular} & \(\surd\) & - & \(\surd\) &
\begin{tabular}{c} under a strong \\ cluttered \\ environment \\ \end{tabular} \\
[24] & \begin{tabular}{c} Small phased array \\ radar \\ Rectangular \\ phased array radar \\ \end{tabular} & \
\begin{tabular}{c} Based on AD9361 \\ Operating \\ Operating \\ frequency: X band \\ Starting frequency \\ is 25 GHz, which is \\ in the 5G band \\ \end{tabular} & \(\surd\) & - & - &
\begin{tabular}{c} Mixed up with birds \\ Detected at 300 m \\ with a speed of \\ \end{tabular} \\
[26] & \begin{tabular}{c} 5G millimeter wave \\ radar \\ \end{tabular} & \
\begin{tabular}{c} Starting frequency \\ is 25 GHz, which is \\ in the 5G band \\ \end{tabular} & \(\surd\) & - & \(\surd\) &
\begin{tabular}{c} 157.9 r/s \& at 850.2 m \\ with a speed of 88 r/s \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of reviewed radar-based techniques for UAV characterization. An acoustic-based detection system was designed and implemented in [30] to detect and locate the UAVs efficiently. The acoustic sensor array configuration comprises two tetrahedron-shaped microphones. The system uses multiple algorithms for data and features extraction from the collected acoustic signals: cepstral coefficients (CC) for extracting the harmonics' features, SVM to classify and distinguish between the extracted features' vectors related to UAV or background noise, and TDOA based on Bayesian framework. Signal processing is concluded with the temporal and dimensional features' vectors calculations to acquire the accurate UAV classification and localization path. They also study the contribution of the SNR in detecting the UAVs against the detection rate. In [36], multi-label UAV sound classification is examined using stacked bidirectional long short-term memory (BiLSTM), an advanced, recurrent neural network (RNN) capable of handling sequence or multiple classification tasks and avoiding long-term dependency issues. The proposed BiLSTM model is 94.02% successful in UAVs' sound classification. Several types of research and studies aim to investigate and evaluate different algorithms used in data acquisition, processing, and classification of the collected acoustic signals. In [31], the system's performance level varies using different audio processing algorithms for characteristic feature vector extraction. The extracted features are inputted  
Figure 4: Overall system diagram of the approach presented in [38]. Figure 5: System overview of the method introduced in [39]. into concurrent neural network (CoNN) for classification. The results confirm better accuracy when integrating CoNN with the Wigner-Ville dictionary than MFCC and mean instantaneous frequency (MIF). The authors in [35] gathered the acoustic data from a local suburban airport for the five samples of commercial multirotor UAVs to establish the performance based on passive acoustic detection.