DocumentCode :
1403274
Title :
Separate-bias estimation with reduced-order Kalman filters
Author :
Haessig, David ; Friedland, Bernard
Author_Institution :
GEC Marconi Hazeltine Corp., Wayne, NJ, USA
Volume :
43
Issue :
7
fYear :
1998
fDate :
7/1/1998 12:00:00 AM
Firstpage :
983
Lastpage :
987
Abstract :
This paper presents the optimal two-stage Kalman filter for systems that involve noise-free observations and constant but unknown bias. Like the full-order separate-bias Kalman filter, this new filter provides an alternative to state vector augmentation and offers the same potential for improved numerical accuracy and reduced computational burden. When dealing with systems involving accurate, essentially noise-free measurements, this new filter offers an additional advantage, a reduction in filter order. The optimal separate-bias reduced order estimator involves a reduced order filter for estimating the state, the order equalling the number of states less the number of observations
Keywords :
Kalman filters; computational complexity; filtering theory; observers; optimisation; uncertain systems; full-order separate-bias Kalman filter; noise-free observations; numerical accuracy; optimal separate-bias reduced order estimator; optimal two-stage Kalman filter; reduced computational burden; reduced-order Kalman filters; separate-bias estimation; state estimation; state vector augmentation; Equations; Filtering; Filters; Noise measurement; Noise reduction; State estimation; Vectors; White noise;
fLanguage :
English
Journal_Title :
Automatic Control, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9286
Type :
jour
DOI :
10.1109/9.701106
Filename :
701106
Link To Document :
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