Application of the Constraint Moving Horizon Estimation Method for GPS/SINS Integrated Navigation System

X. Zhao, H. Qin, L. Cong, D. Yang

Abstract:	SINS/GPS integrated navigation system has become an important research direction in the navigation field. The basic problem of integrated navigation information fusion is how to accurately estimate the carrier state by navigation sensors’ real-time data, namely optimal state estimation problem. Kalman filter (KF) method and extended Kalman filter (EKF) algorithm are mainly adopted in integrated navigation systems to achieve multi-system information fusion and positioning. Although the KF and EKF can be used for state estimation, we might have information about a system that the KF and EKF do not incorporate. Since the performance characteristics of the carrier itself and navigation sensors themselves, there are many constraints in the actual fusion, we can integrate the constraints into the state estimation and get better filtering performance than the KF and EKF provide. Based on this scheme, the constraint moving horizon estimation (MHE) approach can be used for SINS/GPS integrated navigation state estimation. This paper details the application of the constraint moving horizon estimation method for GPS/SINS integrated navigation system. On this basis, simulation experiment is used to verify the validity of the method on fusion precision. The simulation results demonstrate that MHE can effectively utilize the constraints. This algorithm can effectively improve positioning precision of integrated navigation system.
Published in:	Proceedings of the 2014 International Technical Meeting of The Institute of Navigation January 27 - 29, 2014 Catamaran Resort Hotel San Diego, California
Pages:	568 - 573
Cite this article:	Zhao, X., Qin, H., Cong, L., Yang, D., "Application of the Constraint Moving Horizon Estimation Method for GPS/SINS Integrated Navigation System," Proceedings of the 2014 International Technical Meeting of The Institute of Navigation, San Diego, California, January 2014, pp. 568-573.
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