Probabilistic Time of Arrival Localization

15 Oct 2019  ·  Fernando Perez-Cruz, Pablo M. Olmos, Michael Minyi Zhang, Howard Huang ·

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.

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