Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs
- URL: http://arxiv.org/abs/2201.04125v2
- Date: Thu, 13 Jan 2022 14:57:11 GMT
- Title: Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs
- Authors: Raju Shrestha, Daniel Romero, Sundeep Prabhakar Chepuri
- Abstract summary: 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.
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.
To overcome the complexity of this problem, a dynamic programming approach is proposed to obtain lists of waypoints through areas of large uncertainty in linear time.
- Score: 15.452264020787593
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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