Singular value decomposition-based robust cubature Kalman filtering for an integrated GPS/SINS navigation system

Zhang, Qiuzhao; Meng, Xiaolin; Zhang, Shubi; Wang, Yunjia

Abstract

A new nonlinear robust filter is proposed in this paper to deal with the outliers of an integrated Global Positioning System/Strapdown Inertial Navigation System (GPS/SINS) navigation system. The influence of different design parameters for an H∞ cubature Kalman filter is analysed. It is found that when the design parameter is small, the robustness of the filter is stronger. However, the design parameter is easily out of step in the Riccati equation and the filter easily diverges. In this respect, a singular value decomposition algorithm is employed to replace the Cholesky decomposition in the robust cubature Kalman filter. With large conditions for the design parameter, the new filter is more robust. The test results demonstrate that the proposed filter algorithm is more reliable and effective in dealing with the outliers in the data sets produced by the integrated GPS/SINS system.

Citation

Zhang, Q., Meng, X., Zhang, S., & Wang, Y. (2015). Singular value decomposition-based robust cubature Kalman filtering for an integrated GPS/SINS navigation system. Journal of Navigation, 68(3), https://doi.org/10.1017/S0373463314000812

• Journal Article Type: Article
• Acceptance Date: Nov 2, 2014
• Online Publication Date: Nov 25, 2014
• Publication Date: May 1, 2015
• Deposit Date: Jul 25, 2016
• Publicly Available Date: Jul 25, 2016
• Journal: Journal of Navigation
• Print ISSN: 0373-4633
• Electronic ISSN: 1469-7785
• Publisher: Cambridge University Press
• Peer Reviewed: Peer Reviewed
• Volume: 68
• Issue: 3
• DOI: https://doi.org/10.1017/S0373463314000812
• Keywords: Singular Value Decomposition; Cubature Kalman Filter; Integrated Navigation System; Nonlinear Filter
• Public URL: https://nottingham-repository.worktribe.com/output/984010
• Publisher URL: http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=9623825&fileId=S0373463314000812
• Contract Date: Jul 25, 2016