Total Electron Content Monitoring Complemented with Crowdsourced GNSS Observations

    Global Navigation Satellite System (GNSS) is a well-recognized observation technique in studies on the ionosphere due to its sensitivity to the total electron content (TEC). The era of modern smartphones, running on Android version 7.0 and higher, facilitates the acquisition of raw dual-frequency GNSS measurements, paving the way for the GNSS community data to be potentially exploited in geoscience applications. One can assume that the continuous progress in this domain may result in future in a performance of those smart devices reaching the level of GNSS receivers (and antennas) used for atmospheric monitoring. The prospective utilization of a very large number of GNSS-capable smartphones, as a dynamic crowdsourcing receiver network, could form thus an attractive source of complementary GNSS data, allowing to significantly increase the spatial resolution of observations available for the analysis and cover areas of the globe where GNSS receivers are not yet present. The enormous volume of prospective GNSS community data brings, however, major challenges related to data acquisition, its storage, and subsequent processing for deriving various parameters of interest, also in near-real time. The same applies to the analysis of such huge and heterogeneous data sets, requiring a dedicated approach in order to exploit the data in a thorough manner and fully benefit from such a concept.
Application of Machine Learning Technology for GNSS IoT data fusion (CAMALIOT) is an ongoing ESA NAVISP project with activities covering acquisition of GNSS observations from modern smartphones and development of the dedicated infrastructure regarding GNSS processing and machine learning at scale. An Android application, developed within that project, is utilized to retrieve code and phase observations from the modern generation of smartphones. The acquired user-specific data is available to the user in the form of RINEX3-compliant files and can be uploaded by the user to the central server for subsequent processing.
This contribution highlights the CAMALIOT project in relation to the ionosphere and provides information on the developed Android application, data ingestion and processing, complemented with methodology and initial results related to the TEC retrieval based on smartphone data collected in the vicinity of geodetic GNSS receivers, with the latter used for deriving reference time series. Concerning the smartphone data, the amount and quality of observations are much lower compared to the high-grade GNSS equipment and a dedicated pre-processing stage is needed in order to discard bad observations in a proper manner. An apparent correlation between the data quality, utilized frequency bands and satellite constellation involved is visible too. This area of GNSS still suffers from the limitations related mainly to the components comprising the smartphone, resulting in the lower quality of the acquired GNSS observations, compared to those obtained with the use of high-grade GNSS receivers and antennas. This translates to a greater susceptibility to multipath as well as a much more frequent occurrence of observation gaps and cycle slips, affecting the data availability and continuity of the carrier-phase measurements.