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

In the realm of intelligent transportation systems, reliable and precise absolute Connected Automated Vehicles (CAV) positioning is necessary. Global Navigation Satellite Systems (GNSS) provides the foundation for absolute positioning. Recently enhanced Precise Point Positioning (PPP) GNSS, now offering corrections for multi-GNSS on a global scale, offers the potential for PPP GNSS to achieve the 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 multi-GNSS applications, there are many more measurements available than are required to meet the specification, if outlier measurements can be avoided; therefore, measurement selection is important. 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 semidefinite constraint. Second, this article considers multi-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 lowest outlier risk.