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A derivative UKF for tightly coupled INS/GPS integrated navigation

journal contribution
posted on 2024-11-01, 17:44 authored by Gaoge Hu, Shesheng Gao, Yongmin ZhongYongmin Zhong
The tightly coupled INS/GPS integration introduces nonlinearity to the measurement equation of the Kalman filter due to the use of raw GPS pseudorange measurements. The extended Kalman filter (EKF) is a typical method to address the nonlinearity by linearizing the pseudorange measurements. However, the linearization may cause large modeling error or even degraded navigation solution. To solve this problem, this paper constructs a nonlinear measurement equation by including the second-order term in the Taylor series of the pseudorange measurements. Nevertheless, when using the unscented Kalman filter (UKF) to the INS/GPS integration for navigation estimation, it causes a great amount of redundant computation in the prediction process due to the linear feature of system state equation, especially for the case with system state vector in much higher dimension than measurement vector. To overcome this drawback in computational burden, this paper further develops a derivative UKF based on the constructed nonlinear measurement equation. The derivative UKF adopts the concise form of the original Kalman filter (KF) to the prediction process and employs the unscented transformation technique to the update process. Theoretical analysis and simulation results demonstrate that the derivative UKF can achieve higher accuracy with a much smaller computational cost in comparison with the traditional UKF.

History

Journal

ISA Transactions

Volume

56

Start page

135

End page

144

Total pages

10

Publisher

Elsevier

Place published

United States

Language

English

Copyright

© 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Former Identifier

2006052294

Esploro creation date

2020-06-22

Fedora creation date

2015-04-20

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