GNSS Positioning using Cost Function Regulated Multilateration and Graph
  Neural Networks
Amir Jalalirad Davide Belli Bence Major Songwon Jee Himanshu Shah Will Morrison
In urban environments, where line-of-sight signals from GNSS satellites are
frequently blocked by high-rise objects, GNSS receivers are subject to large
errors in measuring satellite ranges. Heuristic methods are commonly used to
estimate these errors and reduce the impact of noisy measurements on
localization accuracy. In our work, we replace these error estimation
heuristics with a deep learning model based on Graph Neural Networks.
Additionally, by analyzing the cost function of the multilateration process, we
derive an optimal method to utilize the estimated errors. Our approach
guarantees that the multilateration converges to the receiver's location as the
error estimation accuracy increases. We evaluate our solution on a real-world
dataset containing more than 100k GNSS epochs, collected from multiple cities
with diverse characteristics. The empirical results show improvements from 40%
to 80% in the horizontal localization error against recent deep learning
baselines as well as classical localization approaches.