In this paper, we take a new approach for time of arrival geo-localization. We show that the main sources of error in metropolitan areas are due to environmental imperfections that bias our solutions, and that we can rely on a probabilistic model to learn and compensate for them. The resulting localization error is validated using measurements from a live LTE cellular network to be less than 10 meters, representing an order-of-magnitude improvement.
@article{arxiv.1910.06569,
title = {Probabilistic Time of Arrival Localization},
author = {Fernando Perez-Cruz and Pablo M. Olmos and Michael Minyi Zhang and Howard Huang},
journal= {arXiv preprint arXiv:1910.06569},
year = {2019}
}