RAIM and AUKF for GNSS Performance Enhancement in Multi-Constellation

With the extensive application and popularization of GPS/BDS/Galileo/GLONASS, about 40 satellites will be tracked at the positioning epoch for GNSS receivers. Thus, satellite selection plays a crucial role in decreasing pressure for receivers with limited data processing capability and enhancing positioning performance by optimal satellite geometric distribution in compatibility and interoperability constellation. We propose a new fast satellite selection algorithm under multi-constellation on account of both Newton’s identities for Geometric Dilution of Precision (GDOP) fast computation and optimal satellite geometric distribution for less cycle calculation. An effective closed-form formula is utilized for GDOP approximation, avoiding conventional matrix inversion with large computational complexity. The objective of this research is to improve GNSS accuracy, availability and reliability in modern receivers. Reliability enhancement usually depends on statistical tests for receiver autonomous integrity monitoring (RAIM) and fault detection and exclusion (FDE) in order to detect and exclude outliers. It is here extended by fast satellite selection algorithm and RAIM for GNSS performance enhancement. The technology of data fusion adopted by the Extended Kalman Filter (EKF) might suffer from the performance degradation and divergence problem due to the first order Taylor linearization process. The model of augmented unscented Kalman filter (AUKF) is employed for data fusion to avoid the above issues linked with modeling error and noise uncertainties. In this paper, we take fast satellite selection algorithm, RAIM and AUKF into consideration to enhance the GNSS position estimates. Simulation results show that we can achieve fairly good performance and provide a new way for modern navigation.