Title
A Novel a Priori State Computation Strategy for the Unscented Kalman Filter to Improve Computational Efficiency.
Abstract
A priori state vector and error covariance computation for the Unscented Kalman Filter (UKF) is described. The original UKF propagates multiple sigma points to compute the a priori mean state vector and the error covariance, resulting in a higher computational time compared to the Extended Kalman Filter (EKF). In the proposed method, the posterior mean state vector is propagated and then the sigma points at the current time step are calculated using the first-order Taylor Series approximation. This reduces the computation time significantly, as demonstrated using two example applications which show improvements of 90.5% and 92.6%. This method shows the estimated state vector and the error covariance are accurate to the first-order Taylor series terms. A second method using Richardson Extrapolation improves prediction accuracy to the second-order Taylor series terms. This is implemented on the two examples, improving efficiency by 85.5% and 86.8%.
Year
DOI
Venue
2017
10.1109/TAC.2016.2599291
IEEE Trans. Automat. Contr.
Keywords
Field
DocType
Kalman filters,Estimation,Taylor series,Difference equations,Noise measurement,Time measurement,Mathematical model
State vector,Extended Kalman filter,Fast Kalman filter,Control theory,Covariance intersection,Unscented transform,Kalman filter,Ensemble Kalman filter,Invariant extended Kalman filter,Mathematics
Journal
Volume
Issue
ISSN
62
4
0018-9286
Citations 
PageRank 
References 
2
0.42
4
Authors
3
Name
Order
Citations
PageRank
Sanat K. Biswas120.42
Li Qiao2334.98
Andrew G. Dempster357771.98