## 1 Introduction  
The occurrence of natural disasters, such as earthquakes, leads to substantial property damage and casualties. In 2017 alone, it is estimated that over 95.6 million people were affected by 335 natural disasters, resulting in an additional loss of 9697 lives and a total cost of USD 335 billion. Promptly searching for and rescuing missing individuals after a disaster strikes is particularly crucial However, the destruction caused by these calamities often renders cellular networks incapable of providing network services, posing challenges in contacting victims within disaster areas and exacerbating search and rescue efforts. Additionally, the presence of obstacles further hampers Global Navigation Satellite System (GNSS) [1] signal propagation commonly used for positioning purposes in disaster zones, leading to either the inability to locate victims or significantly reduced performance levels. Consequently, GNSS systems encounter difficulties when assisting rapid post-disaster search and rescue missions. To overcome the challenges mentioned earlier, drones, commonly referred to as Unmanned Aerial Vehicles (UAVs) [2, 3, 4, 5], have emerged as a valuable resource in locating missing persons in expansive and challenging terrains. Their inherent portability, coupled with ease of deployment and remarkable adaptability, render them ideally suited for this particular task. Recently, deploying 5G New Radio (NR) base stations on drones [6, 7, 8], allows victims' mobile phones to connect to the flying mobile cellular network, facilitating the search and localization of victims. Research efforts predominantly rely on the utilization of the uplink signals for victim localization. [PERSON] et al. [8] measured the Time of Flight (ToF) of a user uplink signal, thus estimating the distance between the user and the UAV. [PERSON] et al. employed a UAV to gather ToF measurement results pertaining to a specific user, and subsequently, devised a pseudo-triangulation algorithm that accurately computes the user's coordinates. This approach efficiently fulfills the objective of victim localization. [PERSON] et al. [9] proposed a three-dimensional (3D) localization framework with uplink DeModulation Reference Signal (DMRS). Distance estimator and angles estimator are designed for 3D localization, and a 3D UAV prediction trajectory algorithm is proposed for updating the coordinates of the UAV. [PERSON] et al. [10] proposed utilizing 5G uplink physical layer channel Sounding Reference Signals (SRSs) for 3D User Equipment (UE) positioning. They contended that the widespread deployment of large-scale antennas in 5G NR base stations enhances the estimation of uplink Angle of Arrival (AoA) derived from SRSs, thereby enabling precise 3D positioning of UEs. However, ref. [10] did not consider the mobility of 5G NR base stations. Ref. [11] proposed a Lightweight Trustworthy Message Exchange (LTME) scheme for UAV networks by efficiently aggregating the cryptography and trust management technologies. The LTME can provide rich functionality and strong robustness with low computation and communication overheads. Ref. [12] proposed an intelligent clustering routing approach (ICRA) for UAV ad hoc networks. In the routing phase, the ICRA can reduce the end-to-end delay and improve the packet delivery rate by introducing inter-cluster forwarding nodes to forward messages among different clusters. Upon a thorough analysis of current research findings, we postulate that in disaster-stricken areas, the uplink transmission power of UEs is significantly constrained, leading to severe attenuation of the uplink signal upon reaching the drone. Consequently, the base station may struggle to receive the signal, resulting in diminished positioning accuracy based on the uplink signal. To address this challenge, this paper introduces a novel approach that harnesses 5G NR beam sweeping technologies to aid the UAV in searching and locating victims.