Case study of Bayesian RAIM algorithm integrated with Spatial Feature Constraint and Fault Detection and Exclusion algorithms for multi‐sensor positioning

Abstract This study proposes three novel integrity monitoring algorithms based on Bayesian Receiver Autonomous Integrity Monitoring (BRAIM). Two problems of for land-based applications GNSS challenging environments are explored: requirements sufficient measurement redundancy and the presence large biases. The need was mitigated by using BRAIM. enabled employment a Fault Detection Exclusion (FDE) algorithm without required minimum availability six measurements. To increase estimated integrity, Spatial Feature Constraint (SFC) implemented to constrain solutions feasible locations within road feature. performance proposed FDE+BRAIM, SFC+BRAIM FDE+SFC+BRAIM evaluated GPS multi-sensor data. For non-Gaussian error distribution under test conditions, best achieved probability misleading information order magnitude 10−8 road-level requirements. results provide an initial proof-of-concept non-linear algorithms.