ARAIM Stochastic Model Refinements for GNSS Positioning Applications in Support of Critical Vehicle Applications

Integrity monitoring (IM) is essential if GNSS positioning technologies are to be fully trusted by future intelligent transport systems. A tighter and conservative stochastic model can shrink protection levels in the position domain and therefore enhance the user-level integrity. In this study, the stochastic models for vehicle-based GNSS positioning are refined in three respects: (1) Gaussian bounds of precise orbit and clock error products from the International GNSS Service are used; (2) a variable standard deviation to characterize the residual tropospheric delay after model correction is adopted; and (3) an elevation-dependent model describing the receiver-related errors is adaptively refined using least-squares variance component estimation. The refined stochastic models are used for positioning and IM under the Advanced Receiver Autonomous Integrity Monitoring (ARAIM) framework, which is considered the basis for multi-constellation GNSS navigation to support air navigation in the future. These refinements are assessed via global simulations and real data experiments. Different schemes are designed and tested to evaluate the corresponding enhancements on ARAIM availability for both aviation and ground vehicle-based positioning applications.

Automated summary

This study improves the stochastic models for vehicle-based GNSS positioning under the ARAIM framework. The refinements include using Gaussian bounds for precise orbit and clock error, adopting variable standard deviation for residual tropospheric delay, and adaptively refining an elevation-dependent model using least-squares variance component estimation. The enhancements are assessed through global simulations and real data experiments, with different schemes designed and tested for aviation and ground vehicle-based positioning applications.