TY - JOUR
T1 - 双层无迹卡尔曼滤波
AU - Yang, Feng
AU - Zheng, Li Tao
AU - Wang, Jia Qi
AU - Pan, Quan
N1 - Publisher Copyright:
Copyright © 2019 Acta Automatica Sinica. All rights reserved.
PY - 2019/7
Y1 - 2019/7
N2 - The unscented Kalman filter (UKF) has the problem of the inaccurate estimation in strong nonlinear systems. To solve this problem, the double layer unscented Kalman filter (DLUKF) algorithm is proposed. In the proposed algorithm, the weighted sampling points are used to represent the prior distribution, and then the inner layer UKF algorithm is used to update each sampling point. Finally, the state estimations are obtained by the update mechanism of the outer layer UKF algorithm. Simulation results show that the proposed algorithm not only has a low computational complexity, but also has a very good estimation accuracy, compared with the existing filtering algorithms.
AB - The unscented Kalman filter (UKF) has the problem of the inaccurate estimation in strong nonlinear systems. To solve this problem, the double layer unscented Kalman filter (DLUKF) algorithm is proposed. In the proposed algorithm, the weighted sampling points are used to represent the prior distribution, and then the inner layer UKF algorithm is used to update each sampling point. Finally, the state estimations are obtained by the update mechanism of the outer layer UKF algorithm. Simulation results show that the proposed algorithm not only has a low computational complexity, but also has a very good estimation accuracy, compared with the existing filtering algorithms.
KW - Improved unscented Kalman filters
KW - Sampling strategy
KW - State estimation
KW - Unscented Kalman filter (UKF)
KW - Unscented particle filter (UPF)
UR - http://www.scopus.com/inward/record.url?scp=85071456579&partnerID=8YFLogxK
U2 - 10.16383/j.aas.c180349
DO - 10.16383/j.aas.c180349
M3 - 文章
AN - SCOPUS:85071456579
SN - 0254-4156
VL - 45
SP - 1386
EP - 1391
JO - Zidonghua Xuebao/Acta Automatica Sinica
JF - Zidonghua Xuebao/Acta Automatica Sinica
IS - 7
ER -