# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 2 UAV Detection and Identification
### RF-Based Detection
#### 2.1.1 Traditional RF-Based Detection  
RF detection involves identifying both the flight control signal and the image transmission signal that are exchanged between the control station and the UAV. Its principle is to analyze the frequency, symbol rate, modulation type, signal jump, channel bandwidth and other information of these signals based on collecting the original RF signal, and extract the 'fingerprint' feature that identifies its uniqueness. To improve the accuracy of identification, researchers often choose to extract multiple features to serve as the 'fingerprint' information for UAVs. In this section, we shall examine the methodologies employed for the extraction of UAV spectral features in recent years. The Fractal dimension (FD) [29] is a statistical metric that characterizes the complexity of a signal, and can also be used to quantify its roughness or irregularity. There are two methods for calculating the FD, namely the time domain method and the phase space domain method. Some commonly used FD include the correlation dimension, Hausdorff dimension, box dimension [40], and so on. The Higuchi algorithm is a highly efficient method for calculating fractal dimensions [41]. The FD has been shown to be able to achieve identification accuracies of up to 100%. Bispectrum is a powerful tool for analyzing nonlinear, non-Gaussian, non-minimal phase stationary random signals. The bispectrum is a powerful tool for reducing the impact of white Gaussian noise (WGN). For better online signal classification and object recognition, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Method** & **Accuracy** \\ \hline  & RCNN & 58.50\% \\  & SPPNet & 59.20\% \\  & Fast RCNN & 70.00\% \\ Vision-based Detection Using Deep Learning & Faster RCNN & 73.20\% \\  & YOLOv3 & 63.40\% \\  & SSD & 76.80\% \\  & RetinaNet & 59.10\% \\ \hline \hline \end{tabular}  
* Traditional RF methods involving RF feature extraction have made progress in recent years. The RF approach based on deep learning uses technology to convert RF signals into images, facilitating feature extraction using deep networks. Traditional acoustic methods mainly rely on Mel-Frequency cepstrum coefficient (MFCC) feature extraction, often supplemented by linear predictive cepstrum coefficient (LPCC). Acoustic methods based on deep learning convert sound signals into spectrograms and extract relevant features. In recent years, vision-based detection methods, especially those based on deep learning, have gained prominence. The models listed in the study use public datasets that are not specifically optimized for UAV detection. However, when these models are optimized and trained on specialized UAV datasets, detection accuracy can often exceed 90%. The effectiveness of UAV detection methods depends on factors such as dataset quality, training techniques, and optimization for specific use cases.  
\end{table} 
Table 2: _Cont._several bispectral-based integrated feature extraction methods have been developed [42], including the axially integrated bispectra (AIB) [30] and the square integrated bispectra (SIB) [31]. The AIB feature and SIB feature exhibit accuracies of 98% and 96%, respectively. Signal Spectrum (SFS) [32] is a graphical representation of the frequency distribution of a signal, which can be used to analyze its frequency content. Throughout the entire flight process of a UAV, the continuous rotation of its propellers induces vibrations that propagate throughout the fuselage. By employing STFT to analyze the received wireless signal, it is possible to distinguish between UAV and non-UAV signals based on differences in their respective SFS. The accuracy of this approach can reach 97.85%. Wavelet energy entropy (WEE) [32] can analyze the change of signal entropy level after wavelet transform. The wavelet transform can capture the sudden short-term signal changes that will occur when the UAV is restored due to the effects of wind and the UAV's movement in three-dimensional space during the flight. The accuracy of the WEE method is 93.75%.