Izvorni znanstveni članak
https://doi.org/10.31534/engmod.2018.3.ri.04d
A hybrid adaptive unscented Kalman filter algorithm
Jun He
; School of Automation, Foshan University, Foshan 528000, CHINA
Yong Chen
; School of Automation, Foshan University, Foshan 528000, CHINA
Zhaoxia Zhang
; School of Automation, Foshan University, Foshan 528000, CHINA
Wentao Yin
; School of Automation, Foshan University, Foshan 528000, CHINA
Danfeng Chen
; School of Automation, Foshan University, Foshan 528000, CHINA
Sažetak
In order to overcome the limitation of the traditional adaptive Unscented Kalman Filtering (UKF) algorithm in noise covariance estimation for state and measurement, we propose a hybrid adaptive UKF algorithm based on combining Maximum a posteriori (MAP) criterion and Maximum likelihood (ML) criterion, in this paper. First, to prevent the actual noise covariance deviating from the true value which can lead to the state estimation error and arouse the filtering divergence, a real-time covariance matrices estimation algorithm based on hybrid MAP and ML is proposed for obtaining the statement and measurement noises covariance, respectively; and then, a balance equation the two kinds of covariance matrix is structured in this proposed to minimize the statement estimation error. Compared with the UKF based MAP and based ML, the proposed algorithm provides better convergence and stability.
Ključne riječi
hybrid adaptive; unscented Kalman filtering; maximum a posteriori; maximum likelihood criterion.
Hrčak ID:
218240
URI
Datum izdavanja:
26.3.2019.
Posjeta: 1.312 *