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

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