eprintid: 14150
rev_number: 7
eprint_status: archive
userid: 5
dir: disk0/00/01/41/50
datestamp: 2016-12-14 10:28:55
lastmod: 2021-08-27 17:41:44
status_changed: 2016-12-14 10:28:55
type: article
metadata_visibility: show
item_issues_count: 1
creators_name: Kurzhanski, A.
creators_id: AL0216
title: On stochastic filtering approximations of estimation problems for systems with uncertainty∗
ispublished: pub
keywords: State estimation, multistage linear system, geometric constraints, convex analysis, Kalman filter
abstract: This paper considers estimating the state of a discrete-time linear system in which the values of an unobserved disturbance process are only known to lie in certain prescribed sets. By introducing additional stochastic disturbances it is shown that this problem can be approximated to arbitrarily high accuracy by the solution of a Kalman filtering problem
date: 1988
date_type: published
publisher: Taylor and Francis
id_number: 10.1080/17442508808833485
creators_browse_id: 2088
full_text_status: none
publication: Stochastics
volume: 23
number: 2
pagerange: 109-130
refereed: TRUE
issn: 0090-9491
coversheets_dirty: FALSE
fp7_type: info:eu-repo/semantics/article
citation:   Kurzhanski, A. <https://pure.iiasa.ac.at/view/iiasa/2088.html>  (1988).  On stochastic filtering approximations of estimation problems for systems with uncertainty∗.   Stochastics 23 (2) 109-130. 10.1080/17442508808833485 <https://doi.org/10.1080/17442508808833485>.