Big Data from Space refers to Space and Earth observation data collected by space-borne and ground-based sensors, as well as other space domains such as Satellite Navigation. Systems in these domains qualify being called “big data” given the sheer volume of sensed data.

In the case of GNSS data the scenario depicted by the storage of digitized intermediate frequency data represents a clear example of Big Data from Space.

Digitized intermediate frequency data is the first and most fundamental measurement available following antenna signal receipt. Due to its data rate, digital data cannot be stored consistently and is converted to lower density measurements such as pseudoranges, code- and carrier phase which generate much lower data rate. The algorithms to derive observables are however specific to each receiver and vendor. The conversion step from IF to observables therefore leads to an unrecoverable loss of information.

The systematic recording of digital IF would allow offline re-processing at any computational speed, using any signal processing technique (e.g. acquisition and tracking algorithms), including those yet to be developed. This might permit the recovery of much more information that can be obtained using observables only. Short samples of this type of data may be useful for GNSS monitoring or identification of vulnerabilities, but also for permanent archiving and later processing for innovative techniques or future scientific applications. For these purposes, some regional/local networks are now collecting IF data during very limited periods of time where the majority of the recorded data is not stored due to the nature of each application.

This activity attempts to develop a pilot system demonstrating the scientific potential derived from systematic recording of digital IF data.  Applications to be prototyped as part of this system would include: innovative processing techniques assessment, identification of environmental effects, interference and other natural or man-made vulnerabilities, liability aspects, feared-events assessment, scientific long-term archiving.

GNSS Big Data information services and resources will be available in this area soon.