- Title
- Modeling continuous-time processes via input-to-state filters
- Creator
- Mahata, Kaushik; Fu, Minyue
- Relation
- Automatica Vol. 42, Issue 7, p. 1073-1084
- Publisher Link
- http://dx.doi.org/10.1016/j.automatica.2006.02.014
- Publisher
- Elsevier
- Resource Type
- journal article
- Date
- 2006
- Description
- A direct algorithm to estimate continuous-time ARMA (CARMA) models is proposed in this paper. In this approach, we first pass the observed data through an input-to-state filter and compute the state covariance matrix. The properties of the state covariance matrix are then exploited to estimate the half-spectrum of the observed data at a set of user-defined points on the right-half plane. Finally, the continuous-time parameters are obtained from the half-spectrum estimates by solving an analytic interpolation problem with a positive real constraint. As shown by simulations, the proposed algorithm delivers much more reliable estimates than indirect modeling approaches, which rely on estimating an intermediate discrete-time model.
- Subject
- continuous-time processes; identification; input-to-state filtering; ARMA modeling
- Identifier
- http://hdl.handle.net/1959.13/26054
- Identifier
- uon:779
- Identifier
- ISSN:0005-1098
- Language
- eng
- Full Text
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