Kalman filtering for matrix estimation

Daniel Choukroun, Haim Weiss, Itzhack Y. Bar-Itzhack, Yaakov Oshman

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

Abstract

A general discrete-time Kalman filter (KF) for state matrix estimation using matrix measurements is presented. The new algorithm evaluates the state matrix estimate and the estimation error covariance matrix in terms of the original system matrices. The proposed algorithm naturally fits systems which are most conveniently described by matrix process and measurement equations. Its formulation uses a compact notation for aiding both intuition and mathematical manipulation. It is a straightforward extension of the classical KF, and includes as special cases other matrix filters that were developed in the past. Beyond the analytical value of the matrix filter, it is shown through various examples arising in engineering problems that this filter can be computationally more efficient than its vectorized version.

Original languageEnglish
Pages (from-to)147-159
Number of pages13
JournalIEEE Transactions on Aerospace and Electronic Systems
Volume42
Issue number1
DOIs
StatePublished - 1 Jan 2006
Externally publishedYes

ASJC Scopus subject areas

  • Aerospace Engineering
  • Electrical and Electronic Engineering

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