Ionospheric and tropospheric characteristics estimation starting from smartphone GNSS raw data. Please register for the Final Presentation of NAVISP EL1-038 "CAMALIOT: Application of Machine Learning Technology for GNSS IoT Data Fusion" project

GNSS infrastructure has been growing significantly in recent years, in the space segment as well as on ground. Millions of Internet-of-things (IoT) devices, including smartphones, use GNSS for positioning. Due to the large number of devices, IoT data offer great potential for GNSS science exploitations, with unprecedented spatio-temporal resolution. However, access to IoT data for scientific purposes is currently limited and the data processing challenging. 

To address these challenges, ESA has launched two parallel contracts. In this final presentation, an overall description of the CAMALIOT project carried out with RINA Consulting-Centro Sviluppo Material S.p.A, the Politecnico di Milano, Intelligentia srl, and Geomatics Research & Development srl will be given. The other project was presented at this event.

In this project, the innovative software infrastructure involved data ingestion, processing and analysis service, implementing IoT components, Machine Learning (ML) models and pipelines. Through an online platform, users have direct access to GNSS Science Data from different IoT sources, benefitting from ML potential to deliver new products. With a modular design, the platform architecture focuses on ease-of-use and flexibility for seamless development and deployment of Big Data and ML pipelines, and adaptability to future changes in the fast-moving world of IoT.

The validation has been conducted through two use cases addressing Ionosphere and Troposphere characterisation. The feasibility of estimating ionospheric and tropospheric products starting from smartphone GNSS raw data was demonstrated with two experimental campaigns in Italy. Both types of products were validated against those derived from geodetic GNSS receivers, and, in the case of the ionospheric Total Electron Content (TEC), against state-of-the-art ionospheric maps.

The presentation will conclude explaining the potential application of the results to future activities.

EL1-038 "CAMALIOT: Application of Machine Learning Technology for GNSS IoT Data Fusion" was funded by NAVISP Element 1, which is dedicated to technology innovation of the European industry in the wide PNT sector.

Please follow the links to the Agenda and to the event registration.