Quantifying navigation integrity is crucial for safety-critical applications of global navigation satellite systems (GNSSs). Traditional receiver autonomous integrity monitoring (RAIM) and advanced RAIM (ARAIM) methods evaluate integrity risk solely based on prior information, such as measurement models and prior fault probabilities. For exploring the benefits of incorporating measurements, previous studies introduced the concept of Bayesian RAIM to evaluate posterior integrity risk. On this basis, this article proposes a Bayesian RAIM approach that offers a guaranteed upper bound on the posterior integrity risk under arbitrary distributions of measurement faults. This is achieved by the following: first, deriving the posterior probability density of the true state; second, establishing a formula to evaluate the posterior integrity risk; and third, searching for the worst-case fault distributions that maximize the posterior integrity risk. The key difference between our approach and the existing method lies in third step: the latter searches the worst-case faults to maximize the posterior probabilities of each hypothesis. Simulation results suggest that the proposed Bayesian RAIM approach offers comparable performance to the ARAIM method in terms of the predictive integrity risk and continuity risk. Meanwhile, the real-time measurement-dependent posterior integrity risk from our approach is lower than the ARAIM integrity risk in most cases.
Bayesian Upper Bound on GNSS Posterior Integrity Risk
IEEE Transactions on Aerospace and Electronic Systems ; 60 , 6 ; 7945-7961
2024-12-01
8693209 byte
Article (Journal)
Electronic Resource
English
Bayesian Fault-Tolerant Position Estimator and Integrity Risk Bound for GNSS Navigation
Online Contents | 2014
|Integrity Risk Allocation for GNSS
British Library Conference Proceedings | 1995
|ROAD VEHICLE INTEGRITY BOUND PROPAGATION USING GNSS/IMU/ODOMETER
British Library Conference Proceedings | 2020
|GNSS Receiver Autonomous Integrity Monitoring
Springer Verlag | 2023
|British Library Online Contents | 2016
|