He, J.; Zhang, Q.; Hu, Q.; Sun, G. A Hybrid Adaptive Unscented Kalman Filter Algorithm. Preprints2017, 2017030127. https://doi.org/10.20944/preprints201703.0127.v1
APA Style
He, J., Zhang, Q., Hu, Q., & Sun, G. (2017). A Hybrid Adaptive Unscented Kalman Filter Algorithm. Preprints. https://doi.org/10.20944/preprints201703.0127.v1
Chicago/Turabian Style
He, J., Qin Hu and Guouxi Sun. 2017 "A Hybrid Adaptive Unscented Kalman Filter Algorithm" Preprints. https://doi.org/10.20944/preprints201703.0127.v1
Abstract
In order to overcome the limitation of the traditional adaptive Unscented Kalman Filtering (UKF) algorithm in noise covariance estimation for statement 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 UFK based MAP and based ML, the proposed algorithm provides better convergence and stability.
Keywords
hybrid adaptive; unscented kalman filtering; maximum a posteriori; maximum likelihood criterion
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
