Distributed Finite-Horizon Extended Kalman Filtering for Uncertain Nonlinear Systems

被引:48
|
作者
Duan, Peihu [1 ]
Duan, Zhisheng [1 ]
Lv, Yuezu [1 ]
Chen, Guanrong [2 ]
机构
[1] Peking Univ, Dept Mech & Engn Sci, Coll Engn, State Key Lab Turbulence & Complex Syst, Beijing 100871, Peoples R China
[2] City Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China
基金
中国国家自然科学基金;
关键词
Distributed extended Kalman filter (DEKF); nonlinear system; sensor network; system uncertainty; STATE ESTIMATION; MULTIAGENT SYSTEMS; CONSENSUS FILTER; COMPLEX NETWORKS; SENSOR NETWORKS; CONTROLLABILITY; LEADER;
D O I
10.1109/TCYB.2019.2919919
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, the state estimation problem is investigated for a class of discrete nonlinear systems via sensor networks. A novel robust distributed extended Kalman filter, which can handle norm-bounded uncertainties in both the system model and its Taylor series expansion, is developed with ensured estimation performance. The filter is distributed in the sense that for each sensor, only its own measurements and its neighbors' information are utilized to optimize the upper bound of the estimation error covariance. Besides, a sufficient condition for the proposed algorithm is derived, which is simple and user-friendly since it depends on the property of the original nonlinear system instead of the estimation error covariance calculated at every step. Finally, the simulation results are presented to demonstrate the effectiveness of the filtering algorithm.
引用
收藏
页码:512 / 520
页数:9
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