Hankel-norm approach to approximate recursive estimation

A. Gombani; M. Pavon

Hankel-norm approach to approximate recursive estimation

A. Gombani, M. Pavon

Mathematics

Research output: Contribution to journal › Conference article › peer-review

Abstract

Let {x(t)} and {y(t)} be multivariate processes which are weakly stationary and stationarily correlated. We consider the problem of finding an approximate recursive low-dimensional filter of x(t), based on the observation of the past of y(t). We use a Hankel-norm approximation scheme to obtain a low order model. We also present some applications to Kalman filtering and to filtering of a signal in additive white noise.

Original language	English (US)
Pages (from-to)	689-694
Number of pages	6
Journal	IFAC Proceedings Series
Volume	2
Issue number	8
State	Published - 1989
Event	Eighth IFAC/IFORS Symposium on Identification and System Parameter Estimation 1988. Part 1 - Beijing, China Duration: Aug 27 1988 → Aug 31 1988

ASJC Scopus subject areas

General Engineering

Cite this

@article{7a42c436c5d044f1bd247f9c8e583e89,

title = "Hankel-norm approach to approximate recursive estimation",

abstract = "Let {x(t)} and {y(t)} be multivariate processes which are weakly stationary and stationarily correlated. We consider the problem of finding an approximate recursive low-dimensional filter of x(t), based on the observation of the past of y(t). We use a Hankel-norm approximation scheme to obtain a low order model. We also present some applications to Kalman filtering and to filtering of a signal in additive white noise.",

author = "A. Gombani and M. Pavon",

year = "1989",

language = "English (US)",

volume = "2",

pages = "689--694",

journal = "IFAC Proceedings Series",

issn = "0741-1146",

number = "8",

note = "Eighth IFAC/IFORS Symposium on Identification and System Parameter Estimation 1988. Part 1 ; Conference date: 27-08-1988 Through 31-08-1988",

}

TY - JOUR

T1 - Hankel-norm approach to approximate recursive estimation

AU - Gombani, A.

AU - Pavon, M.

PY - 1989

Y1 - 1989

N2 - Let {x(t)} and {y(t)} be multivariate processes which are weakly stationary and stationarily correlated. We consider the problem of finding an approximate recursive low-dimensional filter of x(t), based on the observation of the past of y(t). We use a Hankel-norm approximation scheme to obtain a low order model. We also present some applications to Kalman filtering and to filtering of a signal in additive white noise.

AB - Let {x(t)} and {y(t)} be multivariate processes which are weakly stationary and stationarily correlated. We consider the problem of finding an approximate recursive low-dimensional filter of x(t), based on the observation of the past of y(t). We use a Hankel-norm approximation scheme to obtain a low order model. We also present some applications to Kalman filtering and to filtering of a signal in additive white noise.

UR - http://www.scopus.com/inward/record.url?scp=0024941419&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0024941419&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:0024941419

SN - 0741-1146

VL - 2

SP - 689

EP - 694

JO - IFAC Proceedings Series

JF - IFAC Proceedings Series

IS - 8

T2 - Eighth IFAC/IFORS Symposium on Identification and System Parameter Estimation 1988. Part 1

Y2 - 27 August 1988 through 31 August 1988

ER -

Hankel-norm approach to approximate recursive estimation

Abstract

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this