An Adaptive Kalman Filter for SINS/GNSS Integrated Navigation with Inaccurate Process Noise Covariance Matrix Coefficient
- Resource Type
- Conference
- Authors
- Zhu, Fengchi; Zhang, Siqing; Huang, Yulong
- Source
- 2023 IEEE International Conference on Mechatronics and Automation (ICMA) Mechatronics and Automation (ICMA), 2023 IEEE International Conference on. :581-587 Aug, 2023
- Subject
- Bioengineering
Components, Circuits, Devices and Systems
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Accelerometers
Adaptation models
Mechatronics
Automation
Navigation
Gyroscopes
Bayes methods
SINS/GNSS integrated navigation
inaccurate process noise covariance matrix coefficient
nonadjacent state transition model
variational Bayesian approach
sample screening by dimension
- Language
- ISSN
- 2152-744X
Adaptive state estimation with inaccurate process noise covariance matrix (PNCM) has always been a challenge in the SINS/GNSS integrated navigation due to the small magnitude of the PNCM. In this paper, this problem is partially solved by estimating the coefficient of the PNCM. The nonadjacent state transition model is firstly established to get a larger equivalent PNCM, whereby the coefficient can be exactly estimated by using the variational Bayesian method. A sample screening technique by dimension is then proposed to further improve the estimation accuracy of the PNCM coefficient. Simulations and semi-physical experiments validate that the proposed filter can achieve more accurate estimates of the PNCM and navigation error state than the existing state-of-the-art adaptive Kalman filters if the power spectral density ratio of the gyroscope to the accelerometer is known.