Multisensor Nonlinear Fusion Methods Based on Adaptive Ensemble Fifth-Degree Iterated Cubature Information Filter for Biomechatronics

被引:68
作者
Ge, Quanbo [1 ]
Shao, Teng [1 ]
Yang, Qinmin [2 ]
Shen, Xingfa [3 ]
Wen, Chenglin [4 ]
机构
[1] Hangzhou Dianzi Univ, Sch Automat, Inst Syst Sci & Control Engn, Hangzhou 310018, Peoples R China
[2] Zhejiang Univ, Dept Control Sci & Engn, State Key Lab Ind Control Technol, Hangzhou 310027, Peoples R China
[3] Hangzhou Dianzi Univ, Sch Comp Sci & Technol, Hangzhou 310018, Peoples R China
[4] Henan Univ Technol, Coll Elect Engn, Zhengzhou 450001, Peoples R China
来源
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2016年 / 46卷 / 07期
基金
中国国家自然科学基金;
关键词
Biomechatronics; cubature information filter (CIF); ensemble Kalman filter (EsKF); fifth-degree; information fusion; iterated method; performance analysis; KALMAN-FILTER; NUMERICAL-INTEGRATION; SYSTEMS; STATE;
D O I
10.1109/TSMC.2016.2523911
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Performance of the Kalman filter (KF) is degraded when dealing with nonlinear dynamic systems. For a kind of nonlinear biomechatronics system, a fifth-degree ensemble iterated cubature square-root information filter (EsFICIF), which can effectively improve estimation performance, is proposed by combing many estimation schemes. Moreover, the associated multisensor fusion is deeply studied based on this proposed nonlinear filter in this paper. That is, four classic nonlinear fusion methods, which include augmented measurements fusion, weighted measurements fusion, sequential filtering fusion, and distributed filtering fusion, are compared on estimation performance. The motivation of this paper is to extend the work on estimation performance comparison of nonlinear fusion methods based on the conventional extended KF and to validate some basic conclusions existed in the traditional linear data fusion theory based on the proposed EsFICIF. The estimation accuracies of the four nonlinear fusion methods are compared and the exchanging property of measurements update order is also discussed. It is observed that, when the measurement properties are identical, the estimation accuracies of augmented measurements fusion, weighted measurements fusion, and distributed feedback fusion are equivalent, while the sequential filtering fusion does not hold. Furthermore, the exchanging property of the measurements update order of the sequential filtering fusion can no longer be guaranteed. These results further show some basic conclusions existed in linear fusion theory are no longer valid for nonlinear systems and the conclusions based on the EKF are still available for more complex nonlinear filters. Finally, numerical examples are provided to validate the results given in this paper.
引用
收藏
页码:912 / 925
页数:14
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