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Optimal Designs for Steady-State Kalman Filters

  • Guillaume SagnolEmail author
  • Radoslav Harman
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 122)

Abstract

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.

Keywords

Kalman Filter Linear Matrix Inequality Information Matrix Semidefinite Programming Optimal Design Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The research of the second author was supported by the VEGA 1/0163/13 grant of the Slovak Scientific Grant Agency.

Supplementary material

331057_1_En_17_MOESM1_ESM.pdf (130 kb)
(PDF 130 kB)

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Dept. OptimizationZuse Institut BerlinBerlinGermany
  2. 2.Faculty of Mathematics, Physics and InformaticsComenius UniversityBratislavaSlovakia

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