Abstract
This article proposes a robust state estimator with adaptively adjusted observation noise covariance for uncertain linear systems. Since the residuals between observation data and estimation data satisfying the normal distribution, the Mahalanobis distance are known to be Chi-square distributed. We use Chi-square tests to timely distinguish outliers that beyond the confidence interval and adjust the estimated observation noise covariance to a more likely value. Combined with the robust estimation method, an improved algorithm is derived to deal with uncertainties in both system parameters and observation noise covariance. Based on the proposed prediction form, we test the obtained robust state estimator with different deterioration conditions of observation noise covariance and compare it with the estimators without adaptive factor. The simulation results show that the derived state estimator may reduce the accumulation of estimation errors, smooth the estimated state curve, and the performance of the proposed estimator can be significantly improved as the deterioration enlarging.
Highlights
A PPLICATIONS like Free Space Optical Communication (FSO) need Acquisition, Tracking and Pointing (ATP) system to establish reliable channel between aircrafts and satellite earth stations
We introduce adaptive factor into robust state estimator to modify the estimated observation noise covariance based on observed and estimated data
We can see the performance of adaptive factor significantly increases as the actual value of R gradually deteriorate from the presupposed R0
Summary
A PPLICATIONS like Free Space Optical Communication (FSO) need Acquisition, Tracking and Pointing (ATP) system to establish reliable channel between aircrafts and satellite earth stations. Many improvement tasks have been applied on those traditional robust filters, such as taking advantage of the Kalman filter and regularized least-squares to handle model errors in robust state estimator [10]; using sensitivity penalizing on estimation errors to differentiate it from model uncertainties and well compensates nonlinearly uncertainties in state space model [11]; combining the robust state estimator and the fuzzy rule to handle uncertainties in nonlinear model[12]; using Pareto efficient estimator to combine robust filter and Kalman filter by interpolating method, which aims to optimize the convex combination of the nominal regularized residue and the worst-case regularized residue [13]; one inspirational robust state-space estimator who has superiority in concision, i.e., similar algorithm form and parallel computational complexity as Kalman estimator, while it has difficulty in nonlinear estimation problem compared with adaptive fuzzy estimator or sliding mode estimator [14] Those improvement tasks effectively help to compensate target motion model uncertainties but they could not solve observation noise surging problem in proposed scenarios. Three presupposed parameters are used, while in application, they are obtained through mechanism analyzing modeling or experimental modeling
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