Probabilistic Time of Arrival Localization

TL;DR

This paper introduces a probabilistic approach to time of arrival localization that accounts for environmental biases, significantly improving accuracy in urban settings with less than 10 meters error.

Contribution

It presents a novel probabilistic model for TDOA localization that learns and compensates for environmental biases, enhancing urban localization accuracy.

Findings

Localization error less than 10 meters in LTE networks

Order-of-magnitude improvement over previous methods

Effective bias compensation in metropolitan environments

Abstract

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.