A realization approach to stochastic model reduction
Title:
A realization approach to stochastic model reduction
Author:
Desai, Uday B. Pal, Debajyoti Kirkpatrick, Robert D.
Appeared in:
International journal of control
Paging:
Volume 42 (1985) nr. 4 pages 821-838
Year:
1985-10-01
Contents:
The problem of discrete-time stochastic model reduction (approximation) is considered. Using the canonical correlation analysis approach of Akaike (1975), a new order-reduction algorithm is developed. Furthermore, it is shown that the inverse of the reduced-order realization is asymptotically stable. Next, an explicit relationship between canonical variables and the linear least-squares estimate of the state vector is established. Using this, a more direct approach for order reduction is presented, and also a new design for reduced-order Kalman filters is developed. Finally, the uniqueness and symmetry properties for the new realization—the balanced stochastic realization—along with a simulation result, are presented.