The University of Southampton
University of Southampton Institutional Repository

Linear dynamic filtering with noisy input and output

Linear dynamic filtering with noisy input and output
Linear dynamic filtering with noisy input and output
Estimation problems for linear time-invariant systems with noisy input and output are considered. The smoothing problem is a least norm problem. An efficient algorithm using a Riccati-type recursion is derived. The equivalence between the optimal filter and an appropriately modified Kalman filter is established. The optimal estimate of the input signal is derived from the optimal state estimate. The result shows that the noisy input/output filtering problem is not fundamentally different from the classical Kalman filtering problem.
errors-in-variables model, Kalman filtering, optimal smoothing.
0005-1098
167-171
Markovsky, I.
3e68743b-f22e-4b2b-b1a8-2ba4eb036a69
De Moor, B.
f25df85a-5050-448e-bd50-a278455f5b47
Soderstrom, T.
b8b727f0-e8cc-4f68-8e71-39c65adeab19
Markovsky, I.
3e68743b-f22e-4b2b-b1a8-2ba4eb036a69
De Moor, B.
f25df85a-5050-448e-bd50-a278455f5b47
Soderstrom, T.
b8b727f0-e8cc-4f68-8e71-39c65adeab19

Markovsky, I. and De Moor, B. , Soderstrom, T. (ed.) (2005) Linear dynamic filtering with noisy input and output. Automatica, 41 (1), 167-171.

Record type: Article

Abstract

Estimation problems for linear time-invariant systems with noisy input and output are considered. The smoothing problem is a least norm problem. An efficient algorithm using a Riccati-type recursion is derived. The equivalence between the optimal filter and an appropriately modified Kalman filter is established. The optimal estimate of the input signal is derived from the optimal state estimate. The result shows that the noisy input/output filtering problem is not fundamentally different from the classical Kalman filtering problem.

PDF
eivkf_published.pdf - Accepted Manuscript
Download (177kB)
PDF
eivkf_answer.pdf - Other
Download (86kB)

More information

Published date: 2005
Keywords: errors-in-variables model, Kalman filtering, optimal smoothing.
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 263299
URI: https://eprints.soton.ac.uk/id/eprint/263299
ISSN: 0005-1098
PURE UUID: c05bb754-ed44-47fa-a9d4-bad0a54b4b3b

Catalogue record

Date deposited: 06 Jan 2007
Last modified: 18 Jul 2017 07:47

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×