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.

Edge Computing (EC) pushes computational capability to the Terrestrial Devices (TDs), providing more efficient and faster computing solutions. Unmanned Aerial Vehicles (UAVs) equipped with EC servers can be flexibly deployed, even in complex terrains, to provide mobile computing services at all times. Meanwhile, UAVs can establish an air-to-ground line-of-sight link with TDs to improve the quality of communication link. However, the UAV-to-TD link may be obstructed by ground obstacles such as buildings or trees, leading to sub-optimal data transmission rates. To surmount this issue, Reconfigurable Intelligent Surfaces (RISs) emerge as a promising technology capable of intelligently reflecting signals to enhance communication quality between UAVs and TDs. In this paper, we consider the RISs-assisted multi-UAVs collaborative edge Computing Network (RUCN) in urban environment, in which we study the computational offloading problem. Our goal is to maximize the overall energy efficiency of UAVs by jointly optimizing the flight duration and trajectories of UAVs, and the phase shifts of RISs. It is worth noting that this problem has been formally established as NP-hard. Therefore, we propose the Deep Deterministic Policy Gradients based UAV Trajectory and RIS Phase shift optimization algorithm (UTRP-DDPG) to solve this complex optimization challenge. The results of extensive numerical experiments show that the proposed algorithm outperforms the other benchmark algorithms under various parameter settings. Specially, the UTRP-DDPG algorithm improves the UAV energy efficiency by at least 2% compared to DQN algorithm.