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Optimal Designs for Steady-state Kalman filters (2015)

Sagnol, Guillaume ; Harman, Radoslav

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Publication Date: 2020-08-05

Description: 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.

Language: English

Type: conferenceobject , doc-type:conferenceObject

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