Abstract
Among the most critical features in smartphones are the ones related to the GNSS sensor positioning. Although the horizontal values usually deliver reliable results for most location-based services, the elevation value still suffers from reduced accuracy and reliability. This is mostly due to smartphone's related technological and physical limitations, together with environmental conditions that affect the GNSS observations. To overcome this problem, this paper suggests integrating measurements from various smartphone embedded sensors, supplemented with data from external mapping and environmental infrastructures. The use of deep learning models is investigated, aimed at improving the GNSS-based elevation. Evaluations show very promising results in enhancing the height accuracy, closer to the range of the horizontal positioning. This proves the capacity of improving smartphone's GNSS positioning capabilities for location-based services, with the focus on urban and concealed areas.
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