Izvorni znanstveni članak
https://doi.org/10.1080/00051144.2018.1447272
Nonlinear state estimation using neural-cubature Kalman filter
Zhiyong Miao
; Beijing Aerospace Automatic Control Institute, Beijing, China
Yi Zhang
; Beijing Aerospace Automatic Control Institute, Beijing, China
Kun Zhao
; Beijing Aerospace Automatic Control Institute, Beijing, China
Fan Xun
; Beijing Aerospace Automatic Control Institute, Beijing, China
Sažetak
The cubature Kalman filter (CKF) has been widely used in solving nonlinear state estimation problems because of many advantages such as satisfactory filtering accuracy and easy implementation compared to extended Kalman filter and unscented Kalman filter. However, the performance of CKF may degrade due to the uncertainty of the nonlinear dynamic system model. To solve this problem, a neural-cubature Kalman filter (NCKF) algorithm containing a multilayer feed-forward neural network (MFNN) in CKF is proposed to further improve the estimation accuracy and enhance the robustness of CKF. In the proposed NCKF algorithm, the MFNN was used to modify the nonlinear state estimation of CKF as the measurements were processed, and the CKF was used as both a state estimator and an online training paradigm
simultaneously. The experimental results show that the estimation accuracy and robustness of the proposed method are better than those of the CKF, square-root CKF and particle filter.
Ključne riječi
Cubature Kalman filter; nonlinear state estimation; neural-cubature Kalman filter; multilayer feed-forward neural network
Hrčak ID:
203373
URI
Datum izdavanja:
21.3.2018.
Posjeta: 954 *