IEEE Internet of Things Journal, 2020
This paper considers the problem of localizing outdoor ground radio users with the help of an unmanned aerial vehicle (UAV) on the basis of received signal strength (RSS) measurements in an urban environment. We assume that the propagation model parameters are not known a priori, and depending on the UAV location, the UAV-user link can experience either line-of-sight (LoS) or non-line-of-sight (NLoS) propagation condition. We assume that a 3D map of the environment is available which the UAV can exploit in the localization process. Based on the proposed map-aided estimator, we devise an optimal UAV trajectory to accelerate the learning process under
a limited mission time. To do so, we borrow tools such as Fisher information from the theory of optimal experiment design. Our map-aided estimator achieves superior localization accuracy compared to the map-unaware methods, and our simulations
show that optimized UAV trajectory achieves superior learning performance compared to random trajectories.
Systèmes de Communication
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