Abstract
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.
Highlights
State estimation for the dynamic system is an important research field
In order to solve the above challenges of nonlinear robust estimation, we propose a novel adaptive fitting H-infinity filter (AFHF) based on the moving-window technique
It shows that the value of attenuation level in AFHF is adjusted between 0.712 to 5.749, which is different from the fixed solution, i.e., unscented H∞ Kalman filter (UHKF) or fitting H∞ filter (FHF) based on the highly nonlinear and bimodal in univariate nonstationary growth model (UNGM)
Summary
State estimation for the dynamic system is an important research field. Its applications include integrated navigation, fault diagnosis, target tracking, signal processing, information fusion and so on [1]–[3]. In order to solve the above challenges of nonlinear robust estimation, we propose a novel adaptive fitting H-infinity filter (AFHF) based on the moving-window technique. Comparing (11)-(13) with (5), it is obvious that the excessive use of inaccurate residual term zk − Hk x ̄k is overused by increasing Kk in FHF method, in which case,it makes the estimated state xk+1 far from xk+1 (it is close to the real state xk+1) when measurements involve uncertain errors, leading to the biased or even divergent solution. ADAPTIVE FITTING H-INFINITY FILTER AFHF as a robust estimation approach is presented for nonlinear discrete-time systems with disturbance uncertainties. The suboptimal attenuation level is designed to adaptively calculate local attenuation level at each step time
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