Moving-Window-Based Adaptive Fitting H-Infinity Filter for the Nonlinear System Disturbance

• Main Authors: Juan Xia, Shesheng Gao, Yongmin Zhong, Xiaomin Qi, Guo Li, Yang Liu
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Robust estimation; adaptive fitting H-infinity filter; uncertain system disturbance; the noise estimator
• Online Access: https://ieeexplore.ieee.org/document/9072090/

The uncertain disturbance in the system signals can lead to biased state estimates and, in turn, can lead to deterioration in the performance of state estimation for a nonlinear dynamic system. In order to address these issues, this paper develops an adaptive fitting H-infinity filter (AFHF) based moving-window by combining the novel noise estimator with fitting H-infinity filtering. Specifically speaking, the novel noise estimator is designed to estimate the process and measurement noise characteristics during a fixed window epoch on the basic of the moving-window technique. Subsequently, the noise characteristics at each window epoch is regarded as the input noise means and covariances of fitting H-infinity filtering at next epoch. Further, the attenuation level is adaptively calculated at each time step to change the structure of AFHF. The Monte-Carlo simulations and INS/GPS integrated navigation experiments are set up for the sake of verifying the superior performance of the proposed filtering with uncertain disturbances.