Unmanned Aerial Vehicles (UAVs) are promising for their agile flight capabilities, allowing them to carry out tasks in various complex scenarios. The efficiency and accuracy of UAV operations significantly depend on high-precision positioning technology. However, the existing positioning techniques often struggle to achieve accurate position estimates in conditions of Non-Line-Of-Sight (NLOS). To address this challenge, we propose a novel high-precision UAV positioning method based on MultiLayer Perceptron (MLP) integrating Ultra-WideBand (UWB) and Inertial Measurement Unit (IMU) technologies, which can acquire centimeter-level high-precision location estimation. In the method, we simultaneously extract key features from channel impulse responses and state space of UAV for training an MLP model, which can not only reduce error of UWB signals from dynamically flying UAV to anchor in NLOS environments, but also adapt to the diverse environment settings. Specifically, we respectively apply the anchor node assisted position calibration method and cooperative positioning techniques to the dynamic flying UAVs for solving the issues of UWB signal being blocked and lost. We conduct extensive real-world experiments to demonstrate the effectiveness of our approach. The results show that the median positioning errors of UAV in hovering and flight are 6.3 cm and within 20 cm, respectively.