Refine
Year of publication
- 2014 (1) (remove)
Document Type
- ZIB-Report (1)
Language
- English (1)
Has Fulltext
- yes (1) (remove)
Is part of the Bibliography
- no (1)
Keywords
- Kalman Filter (1)
- Optimal Design (1)
- Semidefinite programming (1)
Institute
We consider a stationary discrete-time linear process that can be observed by a finite number of sensors.
The experimental design for the observations consists of an allocation of available resources to these sensors.
We formalize the problem of selecting a design that maximizes the information matrix of the steady-state of the Kalman filter,
with respect to a standard optimality criterion, such as $D-$ or $A-$optimality.
This problem generalizes the optimal experimental design problem for a linear regression model with a finite design space and uncorrelated errors.
Finally, we show that under natural assumptions, a steady-state optimal design can be computed by semidefinite programming.