Prediction optimization method for multi-fault detection enhancement: application to GNSS positioning
Résumé
this paper presents an integrity monitoring method in order to provide a precise Global Navigation Satellite System (GNSS) positioning. The originality of the proposed method consists on robustly select the non-faulty observations subset from GNSS observation by detecting and excluding erroneous measurements. A part of classical Fault Detection and Exclusion (FDE) literature is based on residual using prediction step of a
recursive Bayesian filter like Kalman filter. The confidence granted to the prediction in such methods is critical in the phase of error detection. In GNSS standalone positioning, classical used prediction models are very approximate by inducing bad decisions, which increases the false alarm probability (PFA) and missed detection probability (PMD), leading a diminution in the integrity of GNSS positioning.
In order to improve prediction step accuracy, in this paper, we propose a procedure of prediction optimization using a parametric model in the framework of a RAIM (Receiver Autonomous Integrity Monitoring) residual method used for erroneous measurements detection. Real GNSS data in experimental studies are used to test the proposed method. The results show that prediction optimization method improves RAIM residual sensitivity. In addition, the developed isolation step reduces considerably computational time.