GNSS Positioning using Cost Function Regulated Multilateration and Graph Neural Networks

arXiv:2402.18630v1 Announce Type: new
Abstract: 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 …

abstract arxiv cost cs.lg eess.sp environments errors function graph graph neural networks impact line localization measuring networks neural networks objects reduce satellite satellites type urban