Errors-In-Variables Filtering in Behavioural and State-Space Contexts

Roberto Guidorzi (), Roberto Diversi () and Umberto Soverini ()
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Roberto Guidorzi: Università di Bologna, Dipartimento di Elettronica, Informatica e Sistemistica
Roberto Diversi: Università di Bologna, Dipartimento di Elettronica, Informatica e Sistemistica
Umberto Soverini: Università di Bologna, Dipartimento di Elettronica, Informatica e Sistemistica

A chapter in Total Least Squares and Errors-in-Variables Modeling, 2002, pp 281-291 from Springer

Abstract: Abstract This paper considers the problem of filtering data sequences generated by errors-in-variables processes where all measured signals, differently from the classical Kalman filtering context, are affected by additive noise. The design of optimal (minimal variance) filters leading to estimates of the process inputs and outputs is first carried out in a behavioural context. The state-space context where EIV filtering can be performed relying on modified Kalman filtering techniques is then considered and a Monte Carlo simulation is finally proposed.

Keywords: optimal filtering; dynamic errors-in-variables models; behavioural models; recursive filtering; Kalman filtering. (search for similar items in EconPapers)
Date: 2002
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-94-017-3552-0_25

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DOI: 10.1007/978-94-017-3552-0_25

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