PPP-RTK considering the ionosphere uncertainty with cross-validation

Abstract

With the high-precision products of satellite orbit and clock, uncalibrated phase delay, and the atmosphere delay corrections, Precise Point Positioning (PPP) based on a Real-Time Kinematic (RTK) network is possible to rapidly achieve centimeter-level positioning accuracy. In the ionosphere-weighted PPP–RTK model, not only the a priori value of ionosphere but also its precision affect the convergence and accuracy of positioning. This study proposes a method to determine the precision of the interpolated slant ionospheric delay by cross-validation. The new method takes the high temporal and spatial variation into consideration. A distance-dependent function is built to represent the stochastic model of the slant ionospheric delay derived from each reference station, and an error model is built for each reference station on a five-minute piecewise basis. The user can interpolate ionospheric delay correction and the corresponding precision with an error function related to the distance and time of each reference station. With the European Reference Frame (EUREF) Permanent GNSS (Global Navigation Satellite Systems) network (EPN), and SONEL (Système d'Observation du Niveau des Eaux Littorales) GNSS stations covering most of Europe, the effectiveness of our wide-area ionosphere constraint method for PPP-RTK is validated, compared with the method with a fixed ionosphere precision threshold. It is shown that although the Root Mean Square (RMS) of the interpolated ionosphere error is within 5 cm in most of the areas, it exceeds 10 cm for some areas with sparse reference stations during some periods of time. The convergence time of the 90th percentile is 4.0 and 20.5 min for horizontal and vertical directions using Global Positioning System (GPS) kinematic solution, respectively, with the proposed method. This convergence is faster than those with the fixed ionosphere precision values of 1, 8, and 30 cm. The improvement with respect to the latter three solutions ranges from 10 to 60%. After integrating the Galileo navigation satellite system (Galileo), the convergence time of the 90th percentile for combined kinematic solutions is 2.0 and 9.0 min, with an improvement of 50.0% and 56.1% for horizontal and vertical directions, respectively, compared with the GPS-only solution. The average convergence time of GPS PPP-RTK for horizontal and vertical directions are 2.0 and 5.0 min, and those of GPS + Galileo PPP-RTK are 1.4 and 3.0 min, respectively.

Validating Ionospheric Models Against Technologically Relevant Metrics

“…Precise point positioning algorithms can still benefit from accurate ionospheric solutions, for example, Li et al. (2022).…”

“…However our simple approach still provides a means of quantifying many important aspects of the problem, notably the number and position of GPS satellites visible to a receiver on the ground. Precise point positioning algorithms can still benefit from accurate ionospheric solutions, for example, Li et al (2022).…”

Review of PPP–RTK: achievements, challenges, and opportunities

“…For a proper stochastic model for a wide-area PPP-RTK, developed an ionosphere stochastic model which determines the variance according to the empirical distance exponent and the size of the reference network. Further, Li et al (2022c) proposed a cross-validation method to assess the accuracy of the interpolated slant ionospheric corrections through the high temporal and spatial variation of ionospheric delays.…”

Improved PPP-RTK positioning performance by using the elevation-dependent weighting model for the atmospheric delay corrections

“…Based on a network with an average inter-stations distance of about 117 km, Zha et al (2021) given an empirical distance exponential-dependent variance for ionospheric delay. For more wide-area networks, Li et al (2022) propose a crossvalidation method, taking the high temporal and spatial variation into consideration, thereby shortening the convergence time. However, for mass-market users, two issues should be considered: one is the communication load, that is, the variance information broadcast should not be too much; the other is the density of stations, most of which have an average station distance of less than 100 km.…”