Adaptive Square-Root Unscented Kalman Filter: Implementation of Exponential Forgetting Factor

被引:0
作者
Asl, Reza Mohammadi [1 ]
Hagh, Yashar Shabbouei [1 ]
Fekih, Afef [2 ]
Handroos, Heikki [1 ]
机构
[1] LUT Univ, Lab Intelligent Machines, Lappeenranta, Finland
[2] Univ Louisiana Lafayette, Dept Elect & Comp Engn, Lafayette, LA 70504 USA
来源
2020 6TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND ROBOTICS (ICCAR) | 2020年
关键词
adaptive square-root unscented Kalman filter; state estimation; time varying noise; servo-hydraulic system; STATE;
D O I
10.1109/iccar49639.2020.9108044
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper proposes a new form of adaptive square root unscented Kalman filter that implements an exponential forgetting factor to update the filter. It aims at estimating the states of nonlinear systems without a priori knowledge about the statistics of noises. The filter updates the estimation of covariances of noises with time, and the updated covariances are used to update the states of the system. The proposed approach is implemented to a servo-hydraulic system which states and measurements are affected by time varying noises with time-varying statistics. The obtained results along with the mean square errors of the estimation of states confirmed the performance and precision of the proposed filter.
引用
收藏
页码:622 / 626
页数:5
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