eprintid: 14021
rev_number: 7
eprint_status: archive
userid: 5
dir: disk0/00/01/40/21
datestamp: 2016-11-29 14:27:14
lastmod: 2021-08-27 17:28:08
status_changed: 2016-11-29 14:27:14
type: book_section
metadata_visibility: show
item_issues_count: 2
creators_name: Casti, J.
creators_id: 974
title: A reduced dimensionality method for the steady-state Kalman filter
ispublished: pub
divisions: prog_sds
abstract: We consider the standard Kalman filtering problem in which the dimension of the output (measurement) vector is p, while the dimension of the state-space for the process model is n. The usual approach to determination of the steady-state gain matrix involves solving an algebraic Riccati equation consisting of n(n+1)/2 quadratically nonlinear equations. In this article, we present an alternate equation for the optimal gain matrix, itself, continuing only np quadratically nonlinear components. Numerical results comparing the efficiency of the new equation with the standard approach are also given.
date: 1975-01-01
date_type: published
publisher: Springer Berlin Heidelberg
id_number: 10.1007/BFb0120769
creators_browse_id: 1179
full_text_status: none
series: Mathematical Programming Studies
volume: 5
number: 5
place_of_pub: Germany
pagerange: 116-123
refereed: TRUE
isbn: 978-3-642-00784-2
issn: 0303-3929
book_title: Stochastic Systems: Modeling, Identification and Optimization, I
coversheets_dirty: FALSE
fp7_type: info:eu-repo/semantics/bookPart
citation:   Casti, J. <https://pure.iiasa.ac.at/view/iiasa/1179.html>  (1975).  A reduced dimensionality method for the steady-state Kalman filter.    In:  Stochastic Systems: Modeling, Identification and Optimization, I.  pp. 116-123 Germany: Springer Berlin Heidelberg.  ISBN 978-3-642-00784-2 10.1007/BFb0120769 <https://doi.org/10.1007/BFb0120769>.