GPS Positioning Method of UAV Based on Improved Particle Filter
Qing Xin, Shixun Wang · 2020
With the development of artificial intelligence, particle filter algorithm has become a research hotspot of Chinese and foreign scholars. Since the particle filter algorithm has better performance in the non-linear Gaussian system, the particle filter is applied in the UAV positioning system. The Monte Carlo sampling method is used for the posterior distribution. In view of the particle degradation problem existing in the particle filter algorithm, the re-sampling of the particle filter method is improved. In order to verify the performance of the improved algorithm, experiments were carried out on a quadrotor UAV platform based on STM32. The results show that the improved positioning algorithm can effectively improve the positioning accuracy of the UAV, and has good practicability.