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Amateurs with smartphones help monitor GNSS signals in space

May 20, 2022  - By
This graphic represents measurements uploaded via the CAMALIOT app by thousands of volunteers. (Image: ESA)

This graphic represents measurements uploaded via the CAMALIOT app by thousands of volunteers. (Image: ESA)

More than 11,000 people around Europe and the world have turned their smartphones into GNSS monitoring tools by downloading the CAMALIOT app, so far delivering more than 53 billion measurements of meteorology and space weather patterns to researchers, according to the European Space Agency (ESA).

ESA asks CAMALIOT volunteers to leave their smartphones by a window each night with GNSS on. The phones record small variations in satellite signals, gathering data for machine-learning analysis. More than 50 smartphone models with dual-frequency receivers can use the app.

CAMALIOT was developed through ESA’s Navigation Innovation and Support Programme (NAVISP) with the support of the agency’s Navigation Science Office through its GNSS Science Support Centre. The combination of GNSS data, smartphone access and machine learning in support of science is a priority research line of ESA’s Navigation Science Office.

GNSS signals undergo scintillation as they pass through irregular plasma patches in the ionosphere. This electrically charged upper atmospheric layer is continuously changing, influenced by solar activity, geomagnetic conditions and the local time of day. Dual-frequency GNSS receivers can compensate for this effect by comparing their two frequencies.

As these signals head to Earth, they are also modified by the amount of water vapor in the lower atmosphere, helping to forecast rainfall in particular.

“Fixed satnav stations already monitor these effects, but these smartphone-based measurements are boosting our coverage hugely. We’re very gratified by all the support we’ve received,” said Vicente Navarro, ESA navigation engineer. ”These results will then undergo a Big Data machine-learning analysis, seeking out previously unseen patterns in both Earth and space weather.”

Formally known as the Application of Machine Learning Technology for GNSS IoT Data Fusion project, CAMALIOT is run by a consortium led by ETH Zurich in collaboration with the International Institute for Applied Systems Analysis.

About the Author: Tracy Cozzens

Senior Editor Tracy Cozzens joined GPS World magazine in 2006. She also is editor of GPS World’s newsletters and the sister website Geospatial Solutions. She has worked in government, for non-profits, and in corporate communications, editing a variety of publications for audiences ranging from federal government contractors to teachers.

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