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.
167-171
Markovsky, I.
3e68743b-f22e-4b2b-b1a8-2ba4eb036a69
De Moor, B.
f25df85a-5050-448e-bd50-a278455f5b47
Soderstrom, T.
b8b727f0-e8cc-4f68-8e71-39c65adeab19
2005
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), .
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.
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Published date: 2005
Keywords:
errors-in-variables model, Kalman filtering, optimal smoothing.
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 263299
URI: http://eprints.soton.ac.uk/id/eprint/263299
ISSN: 0005-1098
PURE UUID: c05bb754-ed44-47fa-a9d4-bad0a54b4b3b
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Date deposited: 06 Jan 2007
Last modified: 14 Mar 2024 07:29
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Contributors
Author:
I. Markovsky
Author:
B. De Moor
Editor:
T. Soderstrom
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