Probabilistic Time of Arrival Localization
This work improves localization accuracy for applications in cellular networks, though it appears incremental as it builds on existing time of arrival methods with a new probabilistic approach.
The paper tackles the problem of time of arrival geo-localization in metropolitan areas by addressing environmental imperfections that cause bias, using a probabilistic model to learn and compensate for them, resulting in a localization error of less than 10 meters, which is an order-of-magnitude improvement.
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.