GNSS Datalabs

Nowadays, scientific activities in GNSS are carried out by highly specialised communities. Initiatives like the International GNSS Service contribute to provide data, products and services, on an openly available basis, in support of different science domains. However, despite relationships across domains, scientific exploitation of GNSS data and products is implemented by different vertical systems with ad-hoc mechanisms to exchange information. This approach leads to difficulties in accessing and integrating resources from multiple areas.

Therefore, the GNSS Science Support Centre (GSSC) leverages on mainstream Big Data, Cloud, Virtualisation and Container technologies to address key GNSS science Use Cases, through Machine Learning science pipelines, called Datalabs. Some of our Datalabs include:

  • GSSC Lab: Jupiter notebooks based tool providing, apart from the possibility of running Jupiter Notebooks, text editors, shell and Python consoles in an integrated and flexible manner. GSSC labs also provides generic analysis and decompression tools for GNSS data files.
  • UPC Clocks Viewer: Jupiter notebooks providing tools to analyse and visualise GNSS clock data applying the UPC continuous strategy to eliminate clock jumps between consecutive days.
  • gLab is an interactive educational multipurpose package to process and analyse GNSS data. Among other things gLAB can be used with the practical sessions developed as part to excellent  GNSS introductory course prepared by UPC (vol I and vol II).
  • Octave is a free software and scientific programming language with a powerful mathematics-oriented syntax, featuring built-in 2D/3D plotting and visualization tools and drop-in compatible with a subset of matlab scripts

In general, the GNSS navigation chain is composed of a network of GNSS sensors aiming at collecting some data from a space segment (core-constellation of satellites), and a set of algorithms processing this data to produce a navigation message. Our data labs aims at leveraging the ability of the GNSS community to produce science with the data provided in the GSSC Now platform. The scientific applications are multidomain, some examples are:

  • Modelling of the ionosphere. In this layer, the ionizing radiation from the Sun originates the existence of electrons, in quantities that affect the propagation of radio signals. Correlations across data from crowdsourced and ionospheric enabled GNSS dedicated receivers, would contribute to the definition of ML enhanced Total Electron Content (TEC) maps to model and predict ionospheric parameters relevant for PNT.
  • Interference/man-made vulnerabilities to model PNT error sources. In this domain, the availability of raw data measurements from crowdsourced devices combined with ML techniques can unveil new interference patterns and countermeasures with potential for the introduction of adaptive signal processing algorithms.
  • Estimate accurate and reliable information for retrieval of atmospheric parameters like water vapour or temperature. Measurements or predicted values of these data could be provided as inputs to a high-fidelity atmospheric density model to calculate, in a more precise way, the atmospheric density. In this field, the information gathered from IoT GNSS devices combined with ML algorithms represents another opportunity for a better understanding of weather effects.

Access here our GSSC featured Datalabs for exploring new GNSS science applications.