TY - GEN
T1 - Distributed fault-tolerant fusion estimation based on multiple-model extended kalman filter
AU - Shi, Xiaodi
AU - Yan, Liping
AU - Xia, Yuanqing
N1 - Publisher Copyright:
© 2019 Technical Committee on Control Theory, Chinese Association of Automation.
PY - 2019/7
Y1 - 2019/7
N2 - As practical nonlinear systems become progressively complex, the extended Kalman filter (EKF) is limited for many applications because of its performance degradation. In this paper, we propose a novel multiple-model extended Kalman filter (MMEKF), which remarkably reduced its large deviation. The expansion points designed in the MMEKF algorithm obey the Gaussian distribution in the process of probabilistic models, which are used to approximately represent the whole state space by using multiple probabilistic weighted method. Compared with other filters such as EKF, UKF and CKF, the MMEKF shows higher estimation accuracy for unreliable measurements especially in multi-sensor systems. This paper also considers fault-tolerant distributed data fusion estimation, whose feasibility and effectiveness through a numerical example.
AB - As practical nonlinear systems become progressively complex, the extended Kalman filter (EKF) is limited for many applications because of its performance degradation. In this paper, we propose a novel multiple-model extended Kalman filter (MMEKF), which remarkably reduced its large deviation. The expansion points designed in the MMEKF algorithm obey the Gaussian distribution in the process of probabilistic models, which are used to approximately represent the whole state space by using multiple probabilistic weighted method. Compared with other filters such as EKF, UKF and CKF, the MMEKF shows higher estimation accuracy for unreliable measurements especially in multi-sensor systems. This paper also considers fault-tolerant distributed data fusion estimation, whose feasibility and effectiveness through a numerical example.
KW - Distributed fusion
KW - Multiple-model extended Kalman filter
KW - Probabilistic model design
KW - Unreliable measurements
UR - http://www.scopus.com/inward/record.url?scp=85074444641&partnerID=8YFLogxK
U2 - 10.23919/ChiCC.2019.8866454
DO - 10.23919/ChiCC.2019.8866454
M3 - Conference contribution
AN - SCOPUS:85074444641
T3 - Chinese Control Conference, CCC
SP - 3450
EP - 3455
BT - Proceedings of the 38th Chinese Control Conference, CCC 2019
A2 - Fu, Minyue
A2 - Sun, Jian
PB - IEEE Computer Society
T2 - 38th Chinese Control Conference, CCC 2019
Y2 - 27 July 2019 through 30 July 2019
ER -