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.

该论文提出了一种基于频谱调查的移动机器人测量方法，使用两种算法进行地图估计和信息度量，以及一个动态规划方法规划机器人路径，实现在短时间内在最具信息量的位置进行测量，从而快速构建准确的无线电地图。