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Session D6b: Smartphone-Based Localization

Analysis of Factors Affecting Ionospheric Refraction Estimates Obtained From Smartphone GNSS
Caitlyn Hayden, Ding Yi, Anurag Raghuvanshi, and Sunil Bisnath, York University
Location: Grand Ballroom ABC
Date/Time: Thursday, May. 1, 3:43 p.m.

With the ever expanding capabilities, supply, and demand for smartphone Global Navigation Satellite System (GNSS) technology, the potential for crowd-sourced corrections is rapidly expanding. A key example that has been explored is crowd-sourced ionospheric corrections. With current capabilities set to outperform existing global maps and models in less dense regions, increases in ionospheric refraction estimate quality are an important next step to improve publicly available corrections. Thus, this research explores the potential relationships between various parameters and the estimated ionospheric refraction error in smartphone GNSS. A total of 10 parameters are examined with results indicating high correlations between parameters such as elevation angle, total number of satellites, signal-to-noise ratio, and first frequency pseudorange post-fit residual. Additionally, other parameters such as second frequency pseudorange post-fit residuals and post-fit carrier-phase residuals on both frequencies were found to likely be uncorrelated, and conclusions indicate that they are not expected to be useful in estimating expectations of accuracy for smartphone ionospheric refraction estimates. Additionally, analysis on the performance of different smartphone models is presented. Ultimately, knowing and understanding these relationships and trends will allow an adaptive filtering approach to select the ideal filter duration to increase efficacy, improving the accuracy of individual estimates and the models they are used to generate.
Index Terms—smartphone, ionosphere, GNSS, filtering, elevation angle, signal-to-noise ratio

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