Summary of Gnss Positioning Using Cost Function Regulated Multilateration and Graph Neural Networks, by Amir Jalalirad et al.
GNSS Positioning using Cost Function Regulated Multilateration and Graph Neural Networks
by Amir Jalalirad, Davide Belli, Bence Major, Songwon Jee, Himanshu Shah, Will Morrison
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach is proposed to improve Global Navigation Satellite System (GNSS) receiver accuracy in urban environments, where signal obstructions are common. The authors replace traditional heuristic methods for estimating errors with a Graph Neural Network (GNN)-based deep learning model. By analyzing the multilateration process cost function, an optimal method is derived to utilize these estimated errors, ensuring accurate location estimation as error estimation improves. The solution is evaluated on a real-world dataset of over 100k GNSS epochs from multiple cities, showing improved horizontal localization accuracy (40-80%) compared to recent deep learning baselines and classical approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to improve the accuracy of GPS signals in urban areas where tall buildings block the signal. The usual way to correct these errors is with heuristics, but this approach uses a special type of artificial intelligence called Graph Neural Networks instead. By analyzing how the system works, they developed an optimal method for using these corrections. They tested their solution on a large dataset from many cities and found that it improved GPS accuracy by 40-80% compared to other methods. |
Keywords
* Artificial intelligence * Deep learning * Gnn * Graph neural network
