Abstract
In the application of integrated navigation by using Inertial measurement Units (IMUs) and Global Navigation Satellite System (GNSS), magnetometers and barometer, the process noise of IMU s is usually unstable and considered to be unknown, because temperature and vibration may cause the divergence or an incorrect result of Kalman filter algorithm, especially with the low-cost sensors. To solve the problem of unstable process noise covariance, a Variational Bayesian Adaptive Kalman Filter for integrated navigation is presented in this paper. Given that the conjugate prior distribution of unknown process noise covariance is assumed to be inverse Wishart distribution, Variational Bayesian (VB) method can estimate system state and process noise covariance simultaneously. Based on the idea of VB method, the novel Variational Bayesian adaptive Extended Kalman Filter (VBEKF -P) and Variational Bayesian adaptive Unscented Kalman Filter (VBUKF - P) are implemented and applied to compensate the instability. The comparison experimental results show that VBEKF-P and VBUKF-P can guarantee estimation accuracy and stability under the condition of underestimated process noise covariance, but with the expense of five to ten times of computational complexity.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.