Robust Regional Ionospheric Augmentation Based on IRIM for PPP-RTK

Abstract

Ionospheric corrections are crucial for PPP-RTK. Apart from the deterministic part of ionospheric corrections, the stochastic part is also essential in augmented high-precision positioning. An improper stochastic model will degrade the positioning performance even if the accurate ionospheric corrections are applied. In this paper, the satellite-specific ionospheric residual integrity monitoring (IRIM) index is broadcasted as a part of regional slant ionospheric model. The IRIM index is the 95% quantile of modeling residuals for each satellite representing the accuracy of ionospheric corrections. In the user end, we expand the IRIM index with a factor and regard it as the uncertainty of ionospheric corrections. It shows that 99.78% of uncertainty values wrap the residual. Then, positioning performances are compared among three modes, PPP-AR, PPP-RTK with fixed variance, and PPP-RTK with changed variance based on IRIM. Results show that PPP-RTK with proper variance shows the best performance both in positioning error and convergence time. Compared with PPP-AR, the positioning errors of IRIM-based PPP-RTK reduce from 0.032 m to 0.02m and 0.085 m to 0.044 m in horizontal and vertical direction, respectively. It is similar as the positioning errors of PPP-AR. As for the convergence time, PPP-RTK and IRIM-based PPP-RTK both converge within 1 min. But it takes PPP-AR 4.5 mins to converge.