  Traditional localization algorithms based on features such as time difference
of arrival are impaired by non-line of sight propagation, which negatively
affects the consistency that they expect among distance estimates. Instead,
fingerprinting localization is robust to these propagation conditions but
requires the costly collection of large data sets. To alleviate these
limitations, the present paper capitalizes on the recently-proposed notion of
channel charting to learn the geometry of the space that contains the channel
state information (CSI) measurements collected by the nodes to be localized.
The proposed algorithm utilizes a deep neural network that learns distances
between pairs of nodes using their measured CSI. Unlike standard channel
charting approaches, this algorithm directly works with the physical geometry
and therefore only implicitly learns the geometry of the radio domain.
Simulation results demonstrate that the proposed algorithm outperforms its
competitors and allows accurate localization in emergency scenarios using an
unmanned aerial vehicle.
