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