# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 3 UAV Localization and Tracking
### UAV Localization
#### 3.1.1 RF-Based Localization  
There are a variety of localization methods based on radio frequency, such as Angle of Arrival measurement (AOA), Time of Arrival measurement (TOA), Time Difference of Arrival measurement (TDOA), and RSS measurement. To perform TOA localization, both transmitter and receiver clocks must be synchronized, which is typically difficult to accomplish in anti-UAV scenarios. Similarly, the TDOA positioning method relies on calculating the time difference when the signal arrives at each receiver, which can be challenging to achieve with widely spaced receivers in an anti-UAV system. Additionally, the accuracy of the positioning based on RSS measurement is generally low, and this method has limited applications in UAV positioning. In UAV localization tasks, most studies have focused on localization methods using AOA [29, 86]. Despite the challenges associated with RF-based UAV positioning, these methods have shown promising results and have the advantage of being able to locate both the UAV and the pilot. Future research may focus on developing more accurate and robust RF-based UAV localization methods that can operate in a range of environments and under various UAV operating conditions.  
In recent years, various parametric models have been proposed to compute AOA, such as beamforming, subspace-based methods, cross-correlation-based methods, maximum likelihood methods, and sparse array processing methods. These methods are based on pre-established models for position estimation, which can be cumbersome and computationally intensive. Figure 2 illustrates the specific positioning model. To address these issues, many studies have begun to use machine learning approaches to solve the AOA estimation problem [87, 88]. From experimental results, machine learning-based approaches have shown superiority over traditional methods in terms of processing speed and accuracy. However, these methods may only achieve satisfactory results when the distribution of the training set and the test set are the same, which can be difficult to achieve in practical applications where it is challenging to cover all possible scenarios in the training set. To address these challenges, researchers are now using deep neural networks for emission source localization [86, 89]. Deep Convolutional Neural Networks have been used for enhancing angle classification accuracy, as they allow high-level features to be learned from high-dimensional unstructured data at multiple scales [90]. Compared to traditional MD-based and ML-based methods, CNN-based methods have demonstrated superior accuracy levels, according to simulation results. Table 7[90] displays the advantages and disadvantages of the methods.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Approach** & **Model** & **Advantage** & **Disadvantage** \\ \hline \multirow{4}{*}{Model-driven} & Beamforming [91] & Easy installation & High computational complexity \\  & MUSIC [92] & High accuracy & Vulnerable at high SNR \\  & ESPRIT [93] & Array calibration is not required & Performance degradation of far field \\  & & More complex than MUSIC & High installation cost \\  & & & More complex than MUSIC & Lower accuracy in non-ideal sensor design \\  & & & & High expensive system \\ Machine learning & & CWSVR [94] & Independent on array geometrics & Degrade performance at non \\  & & & identical distributions \\  & & & Less expensive than MD approach & Unable to process high density data \\ Deep learning & & Spectrum CNN [89] & Construct complicated propagation model & Low accuracy at low SNR \\  & & Multi-speaker CNN [96] & Easy to perform at non-identical & High computational expense \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of AOA estimation techniques.