UAV-Aided Efficient Informative Path Planning for Autonomous 3D Spectrum Mapping

作者：Yiran Chen, Qiuming Zhu, Jie Wang, Ziye Jia, Xuan Wang, Zhipeng Lin, Yang Huang, Qihui Wu, César Briso-Rodríguez · 发表于：IEEE Transactions on Cognitive Communications and Networking · 年份：2025 · DOI：10.1109/tccn.2025.3603736 · 被引用次数：13 · 研究领域：Robotics and Sensor-Based Localization、Robotic Path Planning Algorithms、Advanced Image and Video Retrieval Techniques

Three-dimensional (3D) radio environment maps (REMs) can visually represent the spectrum situation over the geographical maps, which facilitates the management of spectrum resources. Recently, the utilization of unmanned aerial vehicles (UAVs) for 3D REM construction has emerged as a promising solution. In this paper, we address the UAV sampling path planning problem by maximizing the information collection along dynamically optimized paths. On this basis, we propose an efficient informative path planning (IPP)-based scheme for autonomous spectrum mapping in unknown environments. Firstly, an effective spectrum data spatial distribution prediction method based on the scalable Gaussian process is introduced. It generates estimated REMs and quantifies associated uncertainty, which serve as the basis for UAV path planning. Then, we develop a hierarchical path planning method. Specifically, an uncertainty-aware target decision strategy is initially designed, which selects informative targets by maximizing the negative integrated posterior variance utility. Then, a local path planner driven by the upper confidence bound criterion is introduced, which utilizes the dynamic programming method to refine the UAV’s path toward the target. The proposed method is validated by a simulated dataset in the campus scenario. Compared to state-of-the-art approaches, simulation results demonstrate that the proposed scheme achieves reductions of 20.37%, 14.85%, and 57.29% in mean absolute error, me...