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

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, mean negative log loss, and mean uncertainty, respectively. Moreover, the time consumption is reduced by 81.65% compared to conventional IPP-based method.