首页|A novel Bayesian-based INS/GNSS integrated positioning method with both adaptability and robustness in urban environments
A novel Bayesian-based INS/GNSS integrated positioning method with both adaptability and robustness in urban environments
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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 comple-mentary 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.
NETL
NSTL
万方数据
