Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/180418
Title: A New Process Uncertainty Robust Student&x2019;s t Based Kalman Filter for SINS/GPS Integration
Authors: Yulong Huang;Yonggang Zhang
Year: 2017
Publisher: IEEE
Abstract: 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&x2019;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&x2019;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&x2019;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.
Description: 
URI: http://localhost/handle/Hannan/180418
volume: 5
More Information: 14391,
14404
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7983360.pdf6.42 MBAdobe PDF
Title: A New Process Uncertainty Robust Student&x2019;s t Based Kalman Filter for SINS/GPS Integration
Authors: Yulong Huang;Yonggang Zhang
Year: 2017
Publisher: IEEE
Abstract: 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&x2019;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&x2019;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&x2019;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.
Description: 
URI: http://localhost/handle/Hannan/180418
volume: 5
More Information: 14391,
14404
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7983360.pdf6.42 MBAdobe PDF
Title: A New Process Uncertainty Robust Student&x2019;s t Based Kalman Filter for SINS/GPS Integration
Authors: Yulong Huang;Yonggang Zhang
Year: 2017
Publisher: IEEE
Abstract: 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&x2019;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&x2019;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&x2019;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.
Description: 
URI: http://localhost/handle/Hannan/180418
volume: 5
More Information: 14391,
14404
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7983360.pdf6.42 MBAdobe PDF