Constrained linear state estimation - A moving horizon approach

Christopher V. Rao, James B. Rawlings, Jay H. Lee

Research output: Contribution to journalArticlepeer-review


This article considers moving horizon strategies for constrained linear state estimation. Additional information for estimating state variables from output measurements is often available in the form of inequality constraints on states, noise, and other variables. Formulating a linear state estimation problem with inequality constraints, however, prevents recursive solutions such as Kalman filtering, and, consequently, the estimation problem grows with time as more measurements become available. To bound the problem size, we explore moving horizon strategies for constrained linear state estimation. In this work we discuss some practical and theoretical properties of moving horizon estimation. We derive sufficient conditions for the stability of moving horizon state estimation with linear models subject to constraints on the estimate. We also discuss smoothing strategies for moving horizon estimation. Our framework is solely deterministic.

Original languageEnglish (US)
Pages (from-to)1619-1628
Number of pages10
Issue number10
StatePublished - Oct 2001
Externally publishedYes


  • Constraints
  • Optimization
  • Stability
  • State estimation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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