TY - CONF A1 - Sagnol, Guillaume A1 - Harman, Radoslav A2 - Steland, Ansgar A2 - Rafajłowicz, Ewaryst A2 - Szajowski, Krzysztof T1 - Optimal Designs for Steady-state Kalman filters T2 - Stochastic Models, Statistics and Their Applications N2 - 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. Y1 - 2015 UR - https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/5366 VL - 122 SP - 149 EP - 157 PB - Springer ER -