liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Identification of the dynamics of time-varying phase aberrations from time histories of the point-spread function
Delft Univ Technol, Netherlands.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Show others and affiliations
2019 (English)In: Optical Society of America. Journal A: Optics, Image Science, and Vision, ISSN 1084-7529, E-ISSN 1520-8532, Vol. 36, no 5, p. 809-817Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
OPTICAL SOC AMER , 2019. Vol. 36, no 5, p. 809-817
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-157542DOI: 10.1364/JOSAA.36.000809ISI: 000466360700013PubMedID: 31045008OAI: oai:DiVA.org:liu-157542DiVA, id: diva2:1328672
Note

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

Available from: 2019-06-22 Created: 2019-06-22 Last updated: 2019-11-27
In thesis
1. Low-rank optimization in system identification
Open this publication in new window or tab >>Low-rank optimization in system identification
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
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.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2019. p. 31
Series
Linköping Studies in Science and Technology. Licentiate Thesis, ISSN 0280-7971 ; 1855
National Category
Control Engineering
Identifiers
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 (English)
Opponent
Supervisors
Funder
EU, European Research Council, 339681Swedish Research Council, E05946CI
Available from: 2019-10-28 Created: 2019-10-28 Last updated: 2020-02-24Bibliographically approved

Open Access in DiVA

fulltext(691 kB)2 downloads
File information
File name FULLTEXT01.pdfFile size 691 kBChecksum SHA-512
bf0dd3bf2194f302e70c8910ae938d061f960581d973fe080295e96dd4e4b5ae30445cbb1e39fed9ab7009207d989015ec3d7012dc793082aa68a9fcde818044
Type fulltextMimetype application/pdf

Other links

Publisher's full textPubMed

Search in DiVA

By author/editor
Klingspor, MånsHansson, AndersLöfberg, Johan
By organisation
Automatic ControlFaculty of Science & Engineering
In the same journal
Optical Society of America. Journal A: Optics, Image Science, and Vision
Other Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 2 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 87 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf