Adaptive H-infinite kalman filter based on multiple fading factors and its application in unmanned underwater vehicle. (February 2021)

• Record Type: Journal Article
• Authors: Wang, Junwei; Chen, Xiyuan; Yang, Ping
• Is Part Of: ISA transactions. Volume 108(2021)
• Page Start: 295
• Page End: 304
• Publication Date: 2021-02
• Subjects: multiple fading factors -- H-infinite kalman filter -- Adaptive threshold -- Unmanned underwater vehicle -- SINS/GPS; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2020.08.030
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: Aiming at the problem that the navigation performances of unmanned underwater vehicle (UUV) may be affected by inaccurate prior navigation information and external environmental interference, which may make the accuracy and reliability of strapdown inertial navigation system (SINS) and global position system (GPS) integrated navigation results worse, positioning divergent and system even invalid, an adaptive H-infinite kalman filtering algorithm based on multiple fading factors (MAHKF) is proposed in this paper. Firstly, the time-varying adaptive fading factor is used to modify the filter parameters on-line to make the initial error of navigation filter converge quickly. Secondly, the H-infinite kalman filter of the SINS/GPS system is built on combining the advantages of robust control, which improved the system robustness under extreme external environment. Further, the adaptive threshold γ of the H-infinite kalman filter is introduced to make the filter adaptive to the environment change. Results of the simulation and experiment demonstrate that the initial error is converged at the beginning stage of navigation process, and the interference from external uncertainty inputs to the integrated navigation system are suppressed effectively with the proposed algorithm. Compared with the conventional kalman filter algorithm (KF), the position errors in three directions of the UUV are reduced by 66.57%, 67.98% and 64.51% respectively with the proposed MAHKF. Highlights: The adaptive threshold γ of the H-infinite kalman filter is proposed to make the H-infinite kalman filter based on multiple fading factors adapt to the environment. The influence of interference inputs on filter is analyzed theoretically. The robustness and accuracy of proposed filter are verified by the simulation and field test.