A novel Bayesian-based INS/GNSS integrated positioning method with both adaptability and robustness in urban environments

Abstract

Achieving higher accuracy positioning results in urban environments at a lower cost has been an important pursuit in areas such as autonomous driving and intelligent transportation. Low-cost Inertial Navigation System and Global Navigation Satellite System (INS/GNSS) integrated navigation systems have been an important means of fulfilling the above quest due to the complementary error characteristics between INS and GNSS. The complex urban driving environment requires the system sufficiently adaptive to keep up with the time-varying measurement noise and sufficiently robust to cope with measurement outliers and prior uncertainties. However, many efforts lack a balance between adaptability and robustness. In this paper, a novel positioning method with both adaptability and robustness is proposed by coupling the Mahalanobis distance method, the Variational Bayesian method and the student’s t-distribution in one process (M-VBt method). This method is robust against non-Gaussian noise and priori uncertainties, plus adaptive against measurement noise uncertainty and time-varying noise. The field test results show that the M-VBt method (especially the Mahalanobis distance part) has significantly improved the system performance in the complex urban driving environment.