# Networked Unmanned Aerial Vehicles for Surveillance and Monitoring: A Survey
## 5 Radio Surveillance by UAV-BSs
Specifically, they developed a bipartite graph matching-based sub-process and dual-domain coordinated descent to identify an optimal user association with nearby UAV-BSs. The authors of [114] investigated the backhaul-aware trajectory optimization of fixed-wing UAV-BSs for continuous available long-time wireless service. In detail, the authors of [114] adopted convex optimization theory to derive optimal resource allocations for both in-band and out-band wireless backhaul schemes of UAV-BSs. The energy-efficient trajectories were obtained by the sequential convex optimization method. To solve the optimal 3D placement of UAV-BSs considering wireless backhaul limitation, autonomous coverage hole detection, and user demand, the authors of [115] took advantage of UAV-BSs' mobility and Q-learning to autonomously detect coverage hole in a targeted area and to then deploy UAV-BSs by considering wireless backhaul limitation with user demand and the core network. Aimed at maximizing the number of associated users, the authors formulated an optimization problem for 3D placement of UAV-BSs at various angular positions and proposed a centralized solution to solve it. Conventional UTDOA Positioning Procedure  
As an integrated central processor in the network, Evolved Serving Mobile Location Center (E-SMLC) requests the position of the user equipment (UE) through the connected base station (BS). UE replies by transmitting a reference signal called Sounding Reference Signal (SRS). Afterwards, the time-of-arrival (TOA) measurements are captured from the SRS received at each BS and sent to E-SMLC. The time-difference-of-arrival (TDOA) between multiple BSs and a reference BS (usually the closest BS to the UE) is then calculated from received TOA information in E-SMLC. Finally, E-SMLC calculates the distance difference between UE to BSs from TDOA and estimates the UE position by solving the resulting hyperbolic equations. The location of each BS is supposed to be known by E-SMLC. The signal transmission process of UTDOA positioning is as illustrated in Figure 10.  
Note that conventional UTDOA positioning requires at least three BSs to locate the UEs. With UAV-BSs' mobility, we think it is possible to conduct UTDOA positioning using a single UAV-BS through the following procedure: \begin{tabular}{l l} \multicolumn{2}{l}{Pessible Solution of UDOA Positioning by UAVBS} \\ \hline
1. & E-SMLC requests at least three times the position of the UE that is covered by a flying UAV-BS. The time gap between each request should be enough for the relative distance between the UE and the UAV-BS to change significantly. \\
2. & UE replies at least three SRSs, received by the UAV-BS successively. Then, TOA measurements based on received SRSs are captured and sent to E-SMLC together with the real-time locations of the UAV-BS while receiving SRSs. \\
3. & E-SMLC calculates TDOA from received TOA measurements and calculates the difference between the distances from the UE to the UAV-BS from TDOA. \\
4. & Ultimately, the UE position is estimated by solving the resulting hyperbolic equations based on the difference in estimate and the UAV-BS' locations as in conventional UTDOA positioning. \\ \end{tabular}   
An illustration of the signal transmission process of the proposed UTDOA using a flying UAV-BS can be seen in Figure 11.  
Figure 10: Signal transmission of conventional UTDOA positioning.