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Identification of the dynamics of time-varying phase aberrations from time histories of the point-spread function
Delft Univ Technol, Netherlands.
Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
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2019 (Engelska)Ingår i: Optical Society of America. Journal A: Optics, Image Science, and Vision, ISSN 1084-7529, E-ISSN 1520-8532, Vol. 36, nr 5, s. 809-817Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

To optimally compensate for time-varying phase aberrations with adaptive optics, a model of the dynamics of the aberrations is required to predict the phase aberration at the next time step. We model the time-varying behavior of a phase aberration, expressed in Zernike modes, by assuming that the temporal dynamics of the Zernike coefficients can be described by a vector-valued autoregressive (VAR) model. We propose an iterative method based on a convex heuristic for a rank-constrained optimization problem, to jointly estimate the parameters of the VAR model and the Zernike coefficients from a time series of measurements of the point-spread function (PSF) of the optical system. By assuming the phase aberration is small, the relation between aberration and PSF measurements can be approximated by a quadratic function. As such, our method is a blind identification method for linear dynamics in a stochastic Wiener system with a quadratic nonlinearity at the output and a phase retrieval method that uses a time-evolution-model constraint and a single image at every time step. (c) 2019 Optical Society of America.

Ort, förlag, år, upplaga, sidor
OPTICAL SOC AMER , 2019. Vol. 36, nr 5, s. 809-817
Nationell ämneskategori
Annan elektroteknik och elektronik
Identifikatorer
URN: urn:nbn:se:liu:diva-157542DOI: 10.1364/JOSAA.36.000809ISI: 000466360700013PubMedID: 31045008OAI: oai:DiVA.org:liu-157542DiVA, id: diva2:1328672
Anmärkning

Funding Agencies|Seventh Framework Programme (FP7) [339681]; Vetenskapsradet (VR) [E05946CI]

Tillgänglig från: 2019-06-22 Skapad: 2019-06-22 Senast uppdaterad: 2019-11-27
Ingår i avhandling
1. Low-rank optimization in system identification
Öppna denna publikation i ny flik eller fönster >>Low-rank optimization in system identification
2019 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

In this thesis, the use of low-rank approximations in connection with problems in system identification is explored. Firstly, the motivation of using low-rank approximations in system identification is presented and the framework for low-rank optimization is derived. Secondly, three papers are presented where different problems in system identification are considered within the described low-rank framework. In paper A, a novel method involving the nuclear norm forestimating a Wiener model is introduced. As shown in the paper, this method performs better than existing methods in terms of finding an accurate model. In paper B and C, a group lasso framework is used to perform input selection in the model estimation which also is connected to the low rank framework. The model structures where these novel methods of input selection is used on are ARX models and state space models, respectively. As shown in the respective papers, these strategies of performing input selection perform better than existing methods in both terms of estimation and input selection.

Ort, förlag, år, upplaga, sidor
Linköping: Linköping University Electronic Press, 2019. s. 31
Serie
Linköping Studies in Science and Technology. Licentiate Thesis, ISSN 0280-7971 ; 1855
Nationell ämneskategori
Reglerteknik
Identifikatorer
urn:nbn:se:liu:diva-161286 (URN)10.3384/lic.diva-161286 (DOI)9789179299743 (ISBN)
Presentation
2019-11-08, Ada Lovelace, B-building, Campus Valla, Linköping, 10:15 (Engelska)
Opponent
Handledare
Forskningsfinansiär
EU, Europeiska forskningsrådet, 339681Vetenskapsrådet, E05946CI
Tillgänglig från: 2019-10-28 Skapad: 2019-10-28 Senast uppdaterad: 2020-02-24Bibliografiskt granskad

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