The widespread application of unmanned aerial vehicles (UAVs) urgently requires an effective tracking algorithm as technical support. Particle filter has been widely applied in maneuvering target tracking, however, there has been no suitable solution to the trade-off between weight degeneracy and particle diversity during the process of resampling. In this paper, we propose an improved particle filter algorithm based on systematic resampling with additional random perturbation. This method ensures that particle filter maintains particle diversity and reduces weight degeneracy under environments with different noise types, simultaneously. The simulation results demonstrate that the proposed algorithm generates more accurate filtered trajectory than generic particle filter, especially under the environment with low noise.