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
Compressed Sensing of Wireless Signals for Image Tensor Reconstruction
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-0019-8411
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-2113-0122
Linköping University, Department of Science and Technology, Physics, Electronics and Mathematics. Linköping University, Faculty of Science & Engineering.
2019 (English)In: IEEE Global Communications Conference: Signal Processing for Communications (Globecom2019 SPC), 2019Conference paper, Published paper (Refereed)
Abstract [en]

Use of wireless signal for identification of unknown object, or technology to see-through a wall to form an image, is gaining growing interest from various fields including law enforcement and military sectors, disaster management, or even in civilian sectors such as construction sites. The great challenge in the implementation of such technology is the stochastic disturbances on wireless signal which will result in a signal with missing samples. Compressive Sensing (CS) is a powerful tool for estimating the missing samples since it can find accurate solution to largely underdetermined linear wireless signals. However, sparse models like CS can also suffer from information loss dues to stochastic lossy nature of wireless, making CS not to have accurate information for reconstruction of a signal. In this paper, we developed a theoretical and experimental framework for the mapping of obstacles by reconstructing the wireless signal based on a sparse signal. We apply tensor format to perform the computations along each mode by relaxing the tensor constraints to obtain accurate results. The proposed framework demonstrates how to take 2D signals, formulate estimate signals and produce a 3D image location in a completely unknown area inside of the obstacle (wall).

Place, publisher, year, edition, pages
2019.
Keywords [en]
Compressed Sensing; Basis Pursuit; Ill-posed; Tensors; Lagrangian; Wireless Signals;Augmented-Lagrangian, Alternating Direction Method of Multipliers, Basis Pursuit, Ill-posed and Inverse Problems, ImagingAugmented-Lagrangian, Alternating Direction Method of Multipliers, Basis Pursuit, Ill-posed and Inverse Problems, Imaging
National Category
Engineering and Technology Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-163576OAI: oai:DiVA.org:liu-163576DiVA, id: diva2:1393006
Conference
IEEE Global Communications Conference, Waikoloa, HI, USA, 9-13 December 2019
Available from: 2020-02-14 Created: 2020-02-14 Last updated: 2020-02-17Bibliographically approved

Open Access in DiVA

No full text in DiVA

Authority records BETA

Fowler, ScottBaravdish, GabrielBaravdish, George

Search in DiVA

By author/editor
Fowler, ScottBaravdish, GabrielBaravdish, George
By organisation
Communications and Transport SystemsFaculty of Science & EngineeringMedia and Information TechnologyPhysics, Electronics and Mathematics
Engineering and TechnologySignal Processing

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 17 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