Recently, with the advancement of technology, unmanned aerial vehicles (UAVs) have had a significant impact on our daily lives. UAVs have gained critical importance due to their potential threat. In this study, the problem of UAV tracks were investigated. The first study deals with a particle filter (PF) and a diffusion map with a Kalman filter (DMK). From the experimental analysis, it is found that both PF and DMK are very suitable for drone tracking because the trajectories of drones are highly uncertain in highly dynamic and noisy environments. To address this problem, we introduce a Kalman filter (KFUEA) for drone tracking based on uncertainty and error. The KFUEA uses regularized least squares (RLS) to minimize measurement errors and provides an appropriate balance between confidence in previous estimates and future measurements. The experiment was conducted to evaluate the performance of KFUEA compared to PF and DMK, taking into account the high uncertainty and noisy UAV tracking environment. The KFUEA algorithm achieved an excellent result in the root mean square error (RMSE) compared to the non-parametric filtering algorithms PF and DMK.