Detection and characterization of equatorial ionospheric scintillation based on GNSS observations using convolutional neural networks

Abstract

Global Navigation Satellite Systems (GNSS) receivers operating in the equatorial region are severely affected by ionospheric scintillation caused by rapid fluctuations in the amplitude and phase of the received electromagnetic wave. To monitor scintillation activity, commercial receivers typically provide the S4 and 𝛔ɸ indices, which are computed using ad-hoc thresholds and detrending techniques. In addition to the intrinsic limitation of being restricted to an M-ary classification problem, these filtering methods are known to mask scintillation events. The present thesis proposes a paradigm shift in the characterization of ionospheric scintillation. Instead of relying on detrending techniques and preset thresholds, recent Bayesian methods are employed to disentangle the scintillation signal from line-of-sight dynamics, and new convolutional neural network algorithms are investigated for the characterization task. A simplified signal model is derived and used to generate synthetic scintillation time series based on two ionospheric scintillation models: the Cornell Scintillation Model and the compact phase-screen-based scintillation model. The main contribution of this thesis is a new framework that combines convolutional neural networks and class activation maps as a post-hoc interpretability tool that highlights input regions most relevant to the discriminative task. This approach makes it possible to analyze samples whose features are prone to be classified as strong rather than weak scintillation, and the converse, without setting arbitrary thresholds or relying on ad-hoc heuristics established to obtain scintillation indices. The results demonstrate an average accuracy above 89%, which enables sample-level characterization of the severity of scintillation and provides significant improvements compared to traditional threshold-based classification methods used in ionospheric scintillation monitoring applications.