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Using invariant extended Kalman filter to integrate inertial navigation system and global positioning system
Mohammad Javad Rajabi1 , Seyyed Mohammad Mehdi Dehghan * 1, Saeed Mohammad-Hosseini1
1- Malek-Ashtar University of Technology
Abstract:   (307 Views)
Using the conventional methods to integrate the inertial navigation system and the global navigation satellite system by the extended Kalman filter depends on a time consuming algorithm to determine relatively accurate initial values. In the presence of large initial error, EKF will diverge or converge slowly due to the dependence of the Jacobians on the state variables values. The invariant extended Kalman filter has the ability to solve this problem by changing the error definition method and using the theory of invariance, in which the convergence does not depend on the error of the initial values. In this paper, in order to remove the initial alignment phase, the IEKF is used to integrate the inertial navigation system and the global positioning system. Considering the navigation variables as elements of a Lie group, the error dynamics can be presented in such a way that the Jacobians are independent of the state variables. The comparison of the integration results using the invariant extended Kalman filter, the standard extended Kalman filter and the error state extended Kalman filter with experimental data set in different initial errors shows the significent performance of the invariant Kalman filter than the other filters and its ability to resolve the divergence or slow convergence problem of the conventional methods. The results show, the dependence of the integration method on the time consuming initial alignment can be removed using this filter.
Keywords: integration, inertial navigation system, global positioning system, large initial Alignment error, invariant Extended Kalman filter
     
Type of Article: Research paper | Subject: Special
Received: 2023/07/18 | Accepted: 2024/02/2 | ePublished ahead of print: 2024/04/13
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