Recurrence plot analysis of GPS ionospheric delay time series in extreme ionospheric conditions

Abstract

With provision of Positioning, Navigation, and Timing (PNT) services, satellite navigation systems have become a pillar of modern society. These services lay the foundations of a growing number of technological and socio-economic systems and constitute a key enabling technology for transportation systems, services and components. Mitigation of disruptions and degradation of Global Navigation Satellite System (GNSS) positioning performance and operation quality become critical issues for satellite navigation advancement and adoption. Ionospheric conditions are the single prime natural cause of GNSS positioning performance disruptions and degradations. Complex, non-linear and random nature of the ionospheric effects on GNSS positioning performance adds to the challenges of the suitable mitigation processes development. Here a contribution to the understanding of the ionospheric effects on GNSS positioning performance is provided through a study of Total Electron Content (TEC) and GNSS pseudorange measurement errors time series in the selected cases of characteristic ionospheric conditions, using the Recurrence Plot Analysis (RPA), a common procedure for studying general time series. Based on experimental GPS observations, this study found good alignment of TEC and TEC-rate time series with several characteristic schemes of dynamical behaviour, thus allowing for classification of ionospheric conditions and related TEC behaviour based on their dynamical properties. Further to this, the study identified several RPA predictors as precursors of developing ionospheric storm and the consequent disruptions and degradation of GNSS positioning performance. The study stressed the importance of TEC time series assessment, and initiates research challenges for consideration of TEC time series RPA predictors for mitigation, correction, and forecasting model development of GNSS pseudorange measurements, and GNSS position estimation errors, thus contributing to GNSS resiliency development against space weather and ionospheric effects.