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Noise reduction in Laguerre-domain discrete delay estimation
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.ORCID iD: 0000-0001-5474-7060
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.ORCID iD: 0000-0002-6608-250x
2022 (English)In: 2022 IEEE 61st Conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2022, p. 6254-6259Conference paper, Published paper (Refereed)
Abstract [en]

This paper introduces a stochastic framework for a recently proposed discrete-time delay estimation method in Laguerre-domain, i.e. with the delay block input and output signals being represented by the corresponding Laguerre series. A novel Laguerre-domain disturbance model allowing the involved signals to be square-summable sequences is devised. The relation to two commonly used time-domain disturbance models is clarified. Furthermore, by forming the input signal in a certain way, the signal shape of an additive output disturbance can be estimated and utilized for noise reduction. It is demonstrated that a significant improvement in the delay estimation error is achieved when the noise sequence is correlated. The noise reduction approach is applicable to other Laguerre-domain problems than pure delay estimation.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022. p. 6254-6259
Series
Proceedings of the IEEE Conference on Decision & Control, ISSN 0743-1546, E-ISSN 2576-2370
National Category
Control Engineering Signal Processing
Identifiers
URN: urn:nbn:se:uu:diva-485808DOI: 10.1109/CDC51059.2022.9992423ISI: 000948128105042ISBN: 978-1-6654-6761-2 (electronic)ISBN: 978-1-6654-6760-5 (electronic)ISBN: 978-1-6654-6762-9 (print)OAI: oai:DiVA.org:uu-485808DiVA, id: diva2:1699598
Conference
IEEE 61st Conference on Decision and Control (CDC), 6-9 December 2022, Cancun, Mexico
Funder
Swedish Research Council, 2019-04451Available from: 2022-09-28 Created: 2022-09-28 Last updated: 2023-05-02Bibliographically approved

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Abdalmoaty, MohamedMedvedev, Alexander

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