NDLI: Extension of the robust two-stage Kalman filtering for systems with unknown inputs

Content Provider	IEEE Xplore Digital Library
Author	Chien-Shu Hsieh
Copyright Year	2007
Description	Author affiliation: Ta Hwa Inst. of Technol., Hsinchu (Chien-Shu Hsieh)
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.
Starting Page	1
Ending Page	4
File Size	96597
Page Count	4
File Format	PDF
ISBN	9781424412716
DOI	10.1109/TENCON.2007.4429133
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-30
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robustness Kalman filters Filtering Geophysical measurements State estimation Geophysics computing Technological innovation Nonlinear filters Noise measurement Computational complexity
Content Type	Text
Resource Type	Article

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