A New Process Uncertainty Robust Student's t Based Kalman Filter for SINS/GPS Integration

Motivated by the problem that the Gaussian assumption of process noise may be violated and the statistics of process noise may be inaccurate when the carrier maneuvers severely, a new process uncertainty robust Student's t-based Kalman filter is proposed to integrate the strap-down inertial nav...

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Bibliographic Details
Published in:IEEE access 2017-01, Vol.5, p.14391-14404
Main Authors: Huang, Yulong, Zhang, Yonggang
Format: Article
Language:English
Subjects:
Bayesian analysis
Covariance matrices
Gaussian process
Global Positioning System
Global positioning systems
GPS
Inertial navigation
Kalman filter
Kalman filters
Maneuvers
Matrix algebra
Matrix methods
Measurement uncertainty
Navigation systems
Noise
Noise measurement
Noise prediction
Parameter estimation
Probability density functions
process uncertainty
Random variables
Robustness
Robustness (mathematics)
Satellite navigation systems
SINS/GPS integration
State space models
State vectors
Student’s t distribution
Uncertainty
variational Bayesian
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Summary:Motivated by the problem that the Gaussian assumption of process noise may be violated and the statistics of process noise may be inaccurate when the carrier maneuvers severely, a new process uncertainty robust Student's t-based Kalman filter is proposed to integrate the strap-down inertial navigation system (SINS) and global positioning system (GPS). To better address the heavy-tailed process noise induced by severe maneuvering, the one-step predicted probability density function is modeled as a Student's t distribution, and the conjugate prior distributions of inaccurate mean vector, scale matrix, and degrees of freedom (dofs) parameter are, respectively, selected as Gaussian, inverse Wishart, and Gamma distributions, based on which a new Student's t-based hierarchical Gaussian state-space model for SINS/GPS integration is constructed. The state vector, auxiliary random variable, mean vector, scale matrix, and dof parameter are jointly estimated based on the constructed hierarchical Gaussian state-space model using the variational Bayesian approach. Experimental results illustrate that the proposed method has significantly better robustness for the suppression of the process uncertainty but slightly higher computational complexity than the existing state-of-the-art methods.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2017.2726519