Title :
Linearized Reduced Order Filtering
Author :
Nagpal, Krishan ; Sims, Craig
Author_Institution :
DEPARTMENT OF ELECTRICAL ENGINEERING, WEST VIRGINIA UNIVERSITY, MORGANTOWN, WEST VIRGINIA 26506
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;
Conference_Titel :
American Control Conference, 1987
Conference_Location :
Minneapolis, MN, USA