频谱测量：使用自主无人机的主动无线电地图估计
Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs
摘要：
无线电地图在无线通信和移动机器人任务中有许多应用，包括资源分配、干扰协调和任务规划。尽管已经提出了许多技术来从空间分布的测量值构建无线电地图，但是假设这些测量值的位置是预先预定的。相比之下，本文提出了频谱测量，其中移动机器人（如无人机（UAV））在主动选择的一组位置处收集测量值，以在短测量时间内获得高质量的地图估计。这分两步进行。首先，设计了两种新算法，基于模型的在线贝叶斯估计器和数据驱动的深度学习算法，用于更新地图估计和指示每个可能位置处的测量信息量的不确定性度量。这些算法提供了互补的优势，并具有每次测量的恒定复杂性。其次，不确定性度量用于规划UAV的轨迹，以在信息量最大的位置处收集测量结果。为了克服这个问题的组合的复杂性，提出了一种动态规划的方法来获得列表的航路点，通过大的不确定性在线性时间的区域。在实际数据集上进行的数值实验证实了该方法能够快速构造出精确的射电图。
英文摘要：
Radio maps find numerous applications in wireless communications and mobile robotics tasks, including resource allocation, interference coordination, and mission planning. Although numerous techniques have been proposed to construct radio maps from spatially distributed measurements, the locations of such measurements are assumed predetermined beforehand. In contrast, this paper proposes spectrum surveying, where a mobile robot such as an unmanned aerial vehicle (UAV) collects measurements at a set of locations that are actively selected to obtain high-quality map estimates in a short surveying time. This is performed in two steps. First, two novel algorithms, a model-based online Bayesian estimator and a data-driven deep learning algorithm, are devised for updating a map estimate and an uncertainty metric that indicates the informativeness of measurements at each possible location. These algorithms offer complementary benefits and feature constant complexity per measurement. Second, the uncertainty metric is used to plan the trajectory of the UAV to gather measurements at the most informative locations. To overcome the combinatorial complexity of this problem, a dynamic programming approach is proposed to obtain lists of waypoints through areas of large uncertainty in linear time. Numerical experiments conducted on a realistic dataset confirm that the proposed scheme constructs accurate radio maps quickly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
