A factor set-based GNSS fault detection and exclusion for vehicle navigation in urban environments

Abstract

With the rapid development of safety–critical applications of Intelligent Transportation Systems, Global Navigation Satellite System (GNSS) fault detection and exclusion (FDE) methods have made navigation systems increasingly reliable. However, in multi-fault cases of urban environments, FDE methods generally demand massive calculations and have a high risk of missed detection and false alarm. To deal with this issue, we proposed a factor set-based FDE algorithm for integrating GNSS and Inertial Measurement Units (IMU). The FDE is first performed efficiently via consistency checking over far fewer subsets of the pseudorange. Afterward, the FDE results are validated by missed-detection and false-alarm checks. The missed-detection check factor is designed by predicting the maximum horizontal GNSS positioning error, while the false-alarm check factor is designed with the aid of IMU mechanization. Following FDE, a loosely coupled GNSS/IMU integration is carried out to output the final estimation of the vehicle's position, velocity and attitude. The proposed algorithm improves both horizontal and 3D positioning accuracy by more than 50% in the field test, compared to the traditional GNSS/IMU loosely coupled scheme. Additionally, with the proposed algorithm, the accuracy improvements of the velocity and the heading are over 20% and 50%, respectively.