Gauss process regression for real-time ionospheric delay estimation from GNSS observations

Abstract The number of devices equipped with global satellite positioning has exceeded seven billion recently. There are a wide variety receivers regarding their accuracy and reliability. Low cost, multi-frequency units have been released on the market latterly; however, single-frequency is still significant. Since measurements influenced by ionospheric delay, accurate ionosphere models utmost importance to reduce effect. This paper summarizes how Gauss process regression (GPR) can be applied derive near real-time regional using raw Global Navigation Satellite System (GNSS) observations permanent stations. While widely used in machine learning, GPR nonparametric, Bayesian approach regression. several benefits for monitoring since it quite robust efficient grid model from data available irregular set pierce points. corresponding instrumental delays estimated parallel Kalman filter. presented algorithm real-time, however results offline calculated compared two high quality TEC map products. Based analysis, modell 2 TECu range. developed methods could efficiently field autonomous vehicle navigation meeting both integrity requirements.