Outlier Accommodation for GNSS Precise Point Positioning using Risk-Averse State Estimation

TL;DR

This work addresses robust real-time GNSS PPP for connected automated vehicles by reframing measurement outlier handling as a measurement-selection problem (RAPS) with a diagonal performance constraint, enabling real-time computation. By leveraging RTPPP corrections, the approach reduces multipath- and CME-induced errors, while the diagonal constraint lowers computational cost relative to full PSD constraints. In experiments with multi-GNSS single-frequency data under open-sky and obstructed conditions, RAPS consistently achieves the lowest estimation risk and superior SAE-compliant positioning performance, particularly in challenging environments, outperforming standard TD and EKF methods by up to 6–19%. The findings demonstrate the practical viability of diagonal-spec RAPS for RT-PPP in automotive applications and point to future extensions to carrier-phase and multi-frequency PPP-RTK with advanced corrections.

Abstract

Reliable and precise absolute positioning is necessary in the realm of Connected Automated Vehicles (CAV). Global Navigation Satellite Systems (GNSS) provides the foundation for absolute positioning. Recently enhanced Precise Point Positioning (PPP) technology now offers corrections for GNSS on a global scale, with the potential to achieve accuracy suitable for real-time CAV applications. However, in obstructed sky conditions, GNSS signals are often affected by outliers; therefore, addressing outliers is crucial. In GNSS applications, there are many more measurements available than are required to meet the specification. Therefore, selecting measurements to avoid outliers is of interest. The recently developed Risk-Averse Performance-Specified (RAPS) state estimation optimally selects measurements to minimize outlier risk while meeting a positive semi-definite constraint on performance; at present, the existing solution methods are not suitable for real-time computation and have not been demonstrated using challenging real-world data or in Real-time PPP (RT-PPP) applications. This article makes contributions in a few directions. First, it uses a diagonal performance specification, which reduces computational costs relative to the positive semi-definite constraint. Second, this article considers GNSS RT-PPP applications. Third, the experiments use real-world GNSS data collected in challenging environments. The RT-PPP experimental results show that among the compared methods: all achieve comparable performance in open-sky conditions, and all exceed the Society of Automotive Engineers (SAE) specification; however, in challenging environments, the diagonal RAPS approach shows improvement of 6-19% over traditional methods. Throughout, RAPS achieves the lowest estimation risk.

Figures (2)

• Figure 1: The top graphs display experimental results for the risk from EKF in blue, EKF with TD in orange, and RAPS in red. The solid green box indicates the period during which the driving was under non-clear view of sky conditions. The bottom graphs display the number of measurements removed by either RAPS or TD (left $y$-axis) and the total number of measurements available (right $y$-axis). The number of measurements available is equivalent to the number used by the EKF.
• Figure 2: The cumulative error distribution of horizontal (top figure) and vertical (bottom figure) positioning errors for EKF in blue, EKF with TD in orange, and RAPS in red for the entire experiment trajectory.