Identification of GPS PNT Degradation Using Analysis of Non-Linear Properties of GPS-Derived TEC Time Series

Detection and characterization of anomalous events leading to GPS/GNSS positioning, navigation, and timing (PNT) degradation is understood as an important contributor in rendering GPS/GNSS PNT resilient. The GPS/GNSS resiliency then fortifies the quality of service (QoS) of GPS/GNSS-based applications. Here the methodological framework is proposed that examines the utilisation of the Lyapunov exponent spectrum and estimated entropy of time-constrained GPS-derived TEC time series for the purpose of detection and characterization of anomalous space weather and ionospheric conditions, as the primary cause of GPS/GNSS PNT degradation. Significance of the obtained results is proposed to be evaluated using robust statistical tests. The proposed detection and characterization methodology is demonstrated in the case of GPS-derived TEC time series taken around the time of the massive earthquake that hit the High Atlas Mountains area around the city of Marrakesh, Morocco on September, 8, 2023. TEC time series are derived from dual-frequency GPS pseudorange observations taken at the International GNSS Service (IGS) reference station in Rabat, Morocco. Analysis of TEC time series non-linear properties is conducted using a bespoke software developed in the R environment for statistical computing. Demonstrated methodology contributes to classification methodology of anomalous GPS/GNSS PNT degradation events, providing novel descriptors/predictors with potential utilisation in the PNT error correction/prediction model development.