Exploring Crowdsourced GNSS Observations For Data Fusion with the Use of Machine Learning
Creators: name: Klopotek, Grzegorz
name: Soja, Benedikt
name: Crocetti, Laura
name: Awadaljeed, Mudathir
name: Pan, Yuanxin
name: Rothacher, Markus
name: See, Linda
name: Weinacker, Rudi
name: Sturn, Tobias
name: McCallum, Ian
name: Navarro, Vicente
Publisher: name: ETH Zurich
Year: 2022
Subjects: crowdscourcing
GNSS
ionosphere
troposphere
machine learning
smartphones

description: Global Navigation Satellite System (GNSS) provides autonomous positioning, navigation, and timing services worldwide. It is also a key space-geodetic technique in many areas of science, such as quantification of global-scale geodynamical phenomena, realization of the International Terrestrial Reference System, or studies on the space weather and atmosphere. Thanks to the continuously operational GNSS, long-term trends of total electron content or precipitable water vapor can be acquired globally and studied subsequently. The era of modern smartphones, running on Android version 7.0 and higher, facilitates nowadays acquisition of raw dual-frequency GNSS measurements from multiple constellations. This paves the way for GNSS community data to be potentially exploited for precise positioning, GNSS reflectometry or geoscience applications at large. Although with lower measurement quality compared to traditional sources of GNSS data, an expansion of ground-based networks of fixed geodetic GNSS receivers with GNSS-capable smartphones would form an interesting source of complementary information for various studies relying on GNSS observations. The ongoing progress in this domain may result in the future in the performance of such smart devices reaching the level of receivers and antennas utilized for atmospheric monitoring. The continuously expanding global GNSS infrastructure along with the enormous volume of prospective GNSS community data bring, however, major challenges related to data acquisition, its storage, and subsequent processing for deriving various parameters of interest. The same applies to the analysis and fusion of auxiliary indices and models with such huge and heterogeneous data sets, requiring a dedicated approach in order to exploit this type of data in a thorough manner and fully benefit from such a concept. Machine Learning (ML) in high-performance computing environments tends to be an appropriate solution for addressing the aforementioned issues. In addition, such large data sets cannot be managed manually anymore, leading thus to the need for fully automated and sophisticated data processing pipelines. 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 ML at scale based on both conventional and crowdsourced GNSS observations as well as auxiliary data sets and models related to troposphere and ionosphere. The crowdsourced campaign, conducted as a part of the project, drew attention of many people and resulted in huge amounts of GNSS data available for the analysis. This contribution provides an overview of the CAMALIOT project with information on the developed Android application for retrieving GNSS observations at scale from the modern generation of smartphones, status of the crowdsourcing campaign, data ingestion and processing, ML-based data quality control, GNSS analysis, and utilization of ML for spatial interpolation and forecasting in relation to ionosphere and troposphere. This contribution is complemented with methodology and results related to ionosphere and troposphere estimates based on smartphone data collected in the vicinity of geodetic receivers, with the latter used for deriving reference time series.