Image: ESA
A new Android app released by the European Space Agency (ESA) turns smartphones equipped with dual-frequency GNSS receivers into instruments for crowdsourced science.
The CAMALIOT app, developed through ESA’s Navigation Innovation and Support Programme (NAVISP) with the support of the GNSS Science Support Centre, is suitable for more than 50 smartphone models.
Using the CAMALIOT app, the phones will record small variations in satellite signals, gathering data for machine learning analysis of meteorology and space weather patterns.
As well as helping to create new Earth and space weather forecasting models, participants are also in with the chance to win prizes including new phones and Amazon vouchers. This four-month “citizen science” campaign runs until the end of July.
“The precisely modulated signals continuously generated by the dozens of GNSS satellites in orbit are proving a valuable resource for science, increasingly employed to study Earth’s atmosphere, oceans and surface environments,” said ESA navigation engineer Vicente Navarro. “Our GNSS Science Support Centre was created to help support this trend.”
For instance, tens of thousands of permanent GNSS stations are continuously recording GNSS data. As the satellite signals travel down to Earth they are modified by the amount of water vapor in the lower atmosphere, helping to forecast rainfall in particular.
GNSS signals also undergo delay and fading — known as 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.
“The combination of Galileo dual band smartphone receivers and Android’s support for raw GNSS data recording is what opened up the prospect of supplementing data from these fixed GNSS stations with tens of millions of smartphones, vastly increasing our density of coverage,” Vincente said. “We took inspiration from the famous ‘SETI@home’ initiative, where home laptops help seek out signs of extraterrestrial life.”
The results can then undergo a Big Data machine learning approach, seeking out previously unseen patterns in both Earth and space weather.
“This is our first step in enlarging GNSS data acquisition using an internet of things data-fusion approach, employing novel sources such as fixed sensors and drones as well as smartphones,” Vincente said. “A wide range of other applications are also possible for the system, including improving the performance of GNSS systems.”
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 (ETHZ) in collaboration with the International Institute for Applied Systems Analysis (IIASA).
“The CAMALIOT effort was underpinned by Element 1 of our NAVISP research programme, spurring innovation in satellite navigation,” said Pierluigi Mancini, ESA’s NAVISP program manager.