Separate-bias estimation with reduced-order Kalman filters

David Haessig, Bernard Friedland

Research output: Contribution to journalArticlepeer-review

45 Scopus citations

Abstract

This paper presents the optimal two-stage Kalman filter for systems that involve noise-free observations and constant but unknown bias Like the full-order separate-bias Kalman filter presented in 1969 [1], this new filter provides an alternative to state vector augmentation and offers the same potential for improved numerical accuracy and reduced computational burden. When dealing with systems involving accurate, essentially noise-free measurements, this new filter offers an additional advantage, a reduction in filter order. The optimal separate-bias reduced-order estimator involves a reduced-order filter for estimating the state, the order equalling the number of states less the number of observations.

Original languageEnglish (US)
Pages (from-to)983-987
Number of pages5
JournalIEEE Transactions on Automatic Control
Volume43
Issue number7
DOIs
StatePublished - 1998

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • Reduced-order Kalman filter
  • Separate-bias estimation
  • Two-stage filtering

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