# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 2 UAV Detection and Identification
### RF-Based Detection
#### 2.1.2 RF-Based Detection Using Deep Learning  
As learning methods continue to evolve, researchers are exploring new ways to improve the effectiveness of signal feature extraction. A wide range of possibilities for signal feature extraction using learning methods have been explored in the literature. Recent research results [43,44] have demonstrated that convolutional neural networks (CNN) are highly suitable for RF-based detection and classification, highlighting the potential of deep learning methods in this field. By leveraging the ability of CNN to automatically extract relevant features from raw signal data, researchers have achieved significant improvements in UAV detection and classification performance. As shown in Ref. [33], CNN can directly extract features from compressed sensing signals without requiring the use of reconstruction algorithms to restore the original signal. Extracting features in this way is a little easier than using traditional methods. The accuracy of the final classification can reach about 99%. In Ref. [34], the authors fed the spectrogram directly into the CNN to extract features. With this method, the prediction accuracy can reach up to 100%. [PERSON] et al. [35] tried to use a time-frequency waterfall map directly to extract features by utilizing a network architecture comprising EESP cells (according to the description in this literature, EESP may refer to the ESP, efficient spatial pyramid [21]) and \begin{table}
\begin{tabular}{l c} \hline
**Fingerprint** & **Accuracy** \\ \hline Fractal dimension (FD) [29] & 100\% \\ Axially integrated bispectra (AIB) [30] & 98\% \\ Square integrated bispectra (SIB) [31] & 96\% \\ Signal Spectrum (SFS) [32] & 97.85\% \\ Wavelet energy entropy (WEE) [32] & 93.75\% \\ Power spectral entropy (PSE) [32] & 83.85\% \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of traditional RF-based detection. the processed RF features are input into the machine learning algorithm (KNN, SVM, RandF) to identify the UAV, and the accuracy rate is optimized. employing the VGG feature extraction method, and achieved good results. This method can achieve an accuracy of over 98%. In Ref. [36], after preprocessing the compressive signal using wavelet transform, the signal was mapped to a two-channel image, which was fed into a VGG-16 model to detect and classify UAVs. Using bispectral feature extraction based on two-dimensional Fourier transform, they were able to extract more frequency domain features. This method is 100% accurate in predicting whether UAVs are present or absent. When the prediction is classified as different types of UAVs, the accuracy rate is 98.9%. When the prediction classification includes different operating modes of various types of UAVs, the accuracy rate is 90.2%. [PERSON] et al. [37] directly converted the two frequency domain dimensions \((f_{1},f_{2})\) and frequency domain amplitude \(|B(f_{1},f_{2})|\) to grayscale images and undertook embedding into a Siamese network, supported by an image augmentation strategy. The accuracy rates for UAV type detection and operation mode detection are 98.57% and 92.31%, respectively. These deep learning-based methods typically convert radio frequency signals into images such as spectrograms, and then use deep learning networks to extract features and identify UAVs. Table 4 presents a comparison of the aforementioned methods. Recent literature on RF-based UAV detection has identified two major development directions. The first direction involves combining UAV physical characteristics with signal processing techniques to extract more effective spectral features, such as waveform energy entropy (WEE) and spectral flatness measure (SFM). This approach aims to boost the discriminative capability of the extracted features by leveraging unique physical properties of UAV signals. The second direction involves developing network models that can extract RF features faster and more accurately.