DocumentCode :
1200566
Title :
Linearized reduced-order filtering
Author :
Nagpal, Krishan ; Sims, Craig
Author_Institution :
Dept. of Electr. Eng., West Virginia Univ., Morgantown, WV, USA
Volume :
33
Issue :
3
fYear :
1988
fDate :
3/1/1988 12:00:00 AM
Firstpage :
310
Lastpage :
313
Abstract :
A reduced-order version of extended Kalman filtering is presented in which both the filtering equation and the associated Riccati equation have been reduced in dimension to allow for real-time processing. The procedure for designing the reduced-order filter is similar to that for designing the extended Kalman filter, the same approximations being applied. One technique useful for limiting the computational burden in a linearized filter design problems is presented and illustrated by an example. The primary limitation of the result is that the nonlinearity must be in terms of the vector to be estimated
Keywords :
Kalman filters; filtering and prediction theory; Kalman filtering; Riccati equation; design; linearized filter; nonlinearity; real-time processing; reduced-order filter; Automatic control; Electrons; Equations; Filtering; Linear systems; Nonlinear filters; Reduced order systems; Root mean square; Stability criteria; State estimation;
fLanguage :
English
Journal_Title :
Automatic Control, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9286
Type :
jour
DOI :
10.1109/9.412
Filename :
412
Link To Document :
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