Title :
Sequence Unscented Kalman Filtering algorithm
Author :
Li, Hui-ping ; Xu, De-min ; Jun, Jiang Li ; Zhang, Fu-bin
Author_Institution :
Marine Coll., Northwest Polytech. Univ., Xian
Abstract :
Unscented Kalman Filter (UKF) has been proved to be a superior alternative to the extended Kalman filter (EKF) when solving the nonlinear system in recent years. In order to improve the real-time of the UKF, A new kind of UKF called Sequence UKF is proposed in this paper. Like Rao-Blackwellised Unscented Kalman Filter (RBUKF) [4], it also deals with nonlinear stochastic discrete-time system with linear measurement equation, however it can decrease the computational complexity with the same filtering accuracy. This algorithm reduces the measurement vector to scalars in measurement-update of UKF by sequence method, so it can avoid inversing the covariance of measurement and reduce a great mount of computation bound. Special algorithm is deduced in this paper. The high performance of sequence UKF is verified by using Monte Carlo simulations.
Keywords :
Kalman filters; Monte Carlo methods; computational complexity; discrete time systems; nonlinear filters; nonlinear systems; stochastic systems; Monte Carlo simulations; RBUKF; Rao-Blackwellised unscented Kalman filter; computational complexity; extended Kalman filter; nonlinear stochastic discrete-time system; nonlinear system; sequence unscented Kalman filtering algorithm; Costs; Covariance matrix; Educational institutions; Filtering algorithms; Kalman filters; Noise measurement; Nonlinear equations; Nonlinear systems; Random variables; Stochastic systems;
Conference_Titel :
Industrial Electronics and Applications, 2008. ICIEA 2008. 3rd IEEE Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-1717-9
Electronic_ISBN :
978-1-4244-1718-6
DOI :
10.1109/ICIEA.2008.4582743