  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.
