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Estimation of continuous-time AR process parameters from discrete-time data
Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering. (Matematisk modellering)
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1999 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 47, no 5, 1232-1244 p.Article in journal (Refereed) Published
Abstract [en]

The problem of estimating continuous-time autoregressive process parameters from discrete-time data is considered. The basic approach used here is based on replacing the derivatives in the model by discrete-time differences, forming a linear regression, and using the least squares method. Such a procedure is simple to apply, computationally flexible and efficient, and may have good numerical properties. It is known, however, that all standard approximations of the highest order derivative, such as repeated use of the delta operator, gives a biased least squares estimate, even as the sampling interval tends to zero. Some of our previous approaches to overcome this problem are reviewed. Then. two new methods, which avoid the shift in our previous results, are presented. One of them, which is termed bias compensation, is computationally very efficient. Finally, the relationship of the above least squares approaches with an instrumental variable method is investigated. Comparative simulation results are also presented

Place, publisher, year, edition, pages
IEEE , 1999. Vol. 47, no 5, 1232-1244 p.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kau:diva-18983DOI: 10.1109/78.757211OAI: oai:DiVA.org:kau-18983DiVA: diva2:592625
Available from: 2013-01-21 Created: 2013-01-21 Last updated: 2013-10-21Bibliographically approved

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