A unified filter for simultaneous input and state estimation of linear discrete-time stochastic systems

Sze Yong, Minghui Zhu, Emilio Frazzoli

Research output: Contribution to journalArticle

58 Citations (Scopus)

Abstract

In this paper, we present a unified optimal and exponentially stable filter for linear discrete-time stochastic systems that simultaneously estimates the states and unknown inputs in an unbiased minimum-variance sense, without making any assumptions on the direct feedthrough matrix. We also provide the connection between the stability of the estimator and a system property known as strong detectability, and discuss the global optimality of the proposed filter. Finally, an illustrative example is given to demonstrate the performance of the unified unbiased minimum-variance filter.

Original languageEnglish (US)
Pages (from-to)321-329
Number of pages9
JournalAutomatica
Volume63
DOIs
StatePublished - Jan 1 2016
Externally publishedYes

Fingerprint

Stochastic systems
State estimation

Keywords

  • Filter stability
  • Input estimation
  • Optimal filtering
  • Recursive filter
  • State estimation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

A unified filter for simultaneous input and state estimation of linear discrete-time stochastic systems. / Yong, Sze; Zhu, Minghui; Frazzoli, Emilio.

In: Automatica, Vol. 63, 01.01.2016, p. 321-329.

Research output: Contribution to journalArticle

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