J. Space Weather Space Clim.
Volume 12, 2022
Topical Issue - Ionospheric plasma irregularities and their impact on radio systems
|Number of page(s)||13|
|Published online||09 May 2022|
Semi-supervised classification of lower-ionospheric perturbations using GNSS radio occultation observations from Spire Global’s Cubesat Constellation
Spire Global, 33 rue Sainte Zithe, 2763 Luxembourg, Luxembourg
2 Spire Global, Skypark 6, 64 – 72 Finnieston Square, Glasgow, G3 8ET, UK
* Corresponding author: firstname.lastname@example.org
Accepted: 24 March 2022
This study presents a new methodology to automatically classify perturbations in the lower ionosphere using GNSS radio occultation (RO) observations collected using Spire’s constellation of CubeSats. This methodology combines signal processing techniques with semi-supervised machine learning by applying spectral clustering in a metric space of wavelet spectra. A “bottom-up” algorithm was applied to extract E layer information directly from Spire’s high-rate (50 Hz) GNSS-RO profiles by subtracting the effect of the F layers. This processing algorithm has been implemented in our ground segment to operationally produce high rate sTEC profiles with a vertical resolution of better than 100 m. The key idea behind the semi-supervised classification is to produce a database of labeled clusters that can be used to classify new unlabeled data by determining which cluster it belongs to. A dataset of more than 12,000 GNSS-RO profiles collected in 2019 containing sTEC perturbations is used to find the initial clusters. This dataset is used to represent the climatology of ionospheric perturbations, such as MSTIDs and sporadic Es. The wavelet power spectrum (WPS) is computed for these profiles, and a metric space is defined using the Earth mover’s distance (EMD) between the WPS. A self-tuning spectral clustering algorithm is used to cluster the profiles in this metric space. These clusters are used as a reference database of perturbations to classify new sTEC profiles by finding the cluster of the closest profile of the clustered dataset in the EMD metric space. This new methodology is used to construct an automated system to monitor ionospheric perturbations on a global scale.
Key words: ionosphere / GNSS-RO / perturbations / classification / cubesats
© G. Savastano et al., Published by EDP Sciences 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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