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Semi-supervised classification of lower-ionospheric perturbations using GNSS radio occultation observations from Spire Global's Cubesat Constellation
Journal of Space Weather and Space Climate
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 bydoi:10.1051/swsc/2022009 fatcat:obzetuqctve6xchfyv6eockrku