OPTIMAL INTERPOLATION FOR LINEAR STOCHASTIC SYSTEMS: THE DISCRETE TIME CASE.

Michael Kohlmann, Michele Pavon

Research output: Contribution to journalConference articlepeer-review

Abstract

The least-squares estimation of the state of a linear discrete-time stochastic system when there is a data gap is considered. The approach, hinging on some basic concepts from stochastic realization theory, makes it possible to derive compact expressions for the optimal estimate in a more direct and illuminating way than would be possible via the present smoothing formulas. As a by-product, the estimation problem for the missing observations is solved.

Original languageEnglish (US)
Pages (from-to)1479-1483
Number of pages5
JournalProceedings of the IEEE Conference on Decision and Control
DOIs
StatePublished - 1984

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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