Title :
Extension of the robust two-stage Kalman filtering for systems with unknown inputs
Author :
Hsieh, Chien-Shu
Author_Institution :
Ta Hwa Inst. of Technol., Hsinchu
fDate :
Oct. 30 2007-Nov. 2 2007
Abstract :
This paper considers the optimal unbiased minimum- variance estimation for systems with unknown inputs that affect both the system model and the measurements. By making use of the recently developed optimal unbiased minimum-variance filter (OUMVF) and a new proposed constrained relationship, an extension of the previous proposed robust two-stage Kalman filter (RTSKF), which is named as the ERTSKF, is presented. The proposed ERTSKF is the optimal solution of two-stage Kalman filters for the considered problem in the sense that it is equivalent to the OUMVF. This is formally established in a theorem. It is also shown that the ERTSKF is computationally more attractive than the OUMVF. Moreover, an alternative to the ERTSKF is also presented to further reduce the computational complexity. Simulation results confirm the effectiveness of the proposed results.
Keywords :
Kalman filters; computational complexity; filtering theory; computational complexity; optimal unbiased minimum-variance filter; robust two-stage Kalman filtering; Computational complexity; Filtering; Geophysical measurements; Geophysics computing; Kalman filters; Noise measurement; Nonlinear filters; Robustness; State estimation; Technological innovation;
Conference_Titel :
TENCON 2007 - 2007 IEEE Region 10 Conference
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-1272-3
Electronic_ISBN :
978-1-4244-1272-3
DOI :
10.1109/TENCON.2007.4429133