DocumentCode
486909
Title
Linearized Reduced Order Filtering
Author
Nagpal, Krishan ; Sims, Craig
Author_Institution
DEPARTMENT OF ELECTRICAL ENGINEERING, WEST VIRGINIA UNIVERSITY, MORGANTOWN, WEST VIRGINIA 26506
fYear
1987
fDate
10-12 June 1987
Firstpage
426
Lastpage
429
Abstract
When a nonlinear dynamical or observational model is used to describe a system, the Kalman filter cannot be used to estimate the state without some approximation being made. If the approximation used is linearization of the equations about the state estimate, the resulting modification of the Kalman filter is often called an extended Kalman filter. In this paper we obtain a similar result, where the filter is constrained to be of reduced order to avoid excessive computational complexity.
Keywords
Atmosphere; Atmospheric modeling; Computational complexity; Filtering; Kalman filters; Linear approximation; Nonlinear equations; Nonlinear filters; Riccati equations; State estimation;
fLanguage
English
Publisher
ieee
Conference_Titel
American Control Conference, 1987
Conference_Location
Minneapolis, MN, USA
Type
conf
Filename
4789358
Link To Document