Unmanned Aerial Vehicles (UAVs) are being widely used in areas like goods delivery, healthcare, agriculture, and surveillance. The latest navigation systems are commonly used for UAV positioning, their performance drops in urban areas due to signal blockages, interference, and non-line-of-sight issues caused by buildings and other structures. To address these challenges, 5G networks offer better coverage, high data rates, and lower delays, making them suitable for UAV tracking in urban air mobility environments. This study focuses on using 5G Time-of-Arrival (TOA) signals to estimate and track the position of UAVs in such environments. A multi-object tracking algorithm is used to process these signals and three different filters viz. Constant Velocity Extended Kalman (CVEK), Particle Filter (PF), and Unscented Kalman Filter (UKF) are evaluated for their accuracy and runtime. The filters are compared using mean absolute error (MAE) and root mean square error (RMSE) performance metrics. The simulation results show that the CVEK filter provides the highest tracking accuracy, while the UKF offers the fastest runtime. This work helps to identify the most suitable tracking approach for real-time UAV operations in urban air mobility scenarios.