Learning GNSS Positioning Corrections for Smartphones using Graph Convolution Neural Networks

Adyasha Mohanty and Grace Gao

Abstract: Smartphone receivers comprise about 1.5 billion GNSS receivers currently manufactured in the world. Smartphone receivers provide measurements with lower signal levels and higher noise. Due to constraints on size, weight, power consumption, and cost, it is challenging to provide accurate positioning with these receivers, especially in urban environments. Traditionally, the GPS measurements are processed using model-based approaches, such as Weighted Least-Squares and Kalman filtering. While model-based approaches can provide meter-level positioning accuracy in a post-processing manner, they need strong assumptions on the noise models and require manual tuning of parameters such as covariances. In contrast, learning-based approaches have been proposed that make less assumptions about the data structure and can accurately model environment-specific errors. However, these approaches provide lower accuracy than model-based methods and are sensitive to initialization. In this paper, we propose a hybrid framework for learning position correction which is the offset between the true receiver position and the estimated position. For learning-based approach, we propose a Graph Convolution Neural Network (GCNN) that can learn different graph structures with multi-constellation and multi-frequency signals. For better initialization of the GCNN, we use a Kalman filter to estimate a coarse receiver position. We also use such a coarse receiver position to condition the input features to the graph. We test our proposed approach on the Google Smartphone Decimeter Challenge (GSDC) real-world datasets and show improved positioning performance over model-based methods such as Weighted Least-Squares and Kalman filter.
Published in: Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022)
September 19 - 23, 2022
Hyatt Regency Denver
Denver, Colorado
Pages: 2215 - 2225
Cite this article: Updated citation: Published in NAVIGATION: Journal of the Institute of Navigation
Full Paper: ION Members/Non-Members: 1 Download Credit
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