Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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
Bayesian strategies for calibrating heteroskedastic static sensors with unknown model structures
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-6868-2210
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0002-4310-7938
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0002-0079-9049
Show others and affiliations
2018 (English)In: 2018 European Control Conference (ECC), Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 2447-2453Conference paper, Published paper (Refereed)
Abstract [en]

This paper investigates the problem of calibrating sensors affected by (i) heteroskedastic measurement noise and (ii) a polynomial bias, describing a systematic distortion of the measured quantity. First, a set of increasingly complex statistical models for the measurement process was proposed. Then, for each model the authors design a Bayesian parameters estimation method handling heteroskedasticity and capable to exploit prior information about the model parameters. The Bayesian problem is solved using MCMC methods and reconstructing the unknown parameters posterior in sampled form. The authors then test the proposed techniques on a practically relevant case study, the calibration of Light Detection and Ranging (Lidar) sensor, and evaluate the different proposed procedures using both artificial and field data.

Place, publisher, year, edition, pages
Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2018. p. 2447-2453
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-72550DOI: 10.23919/ECC.2018.8550201Scopus ID: 2-s2.0-85059819837ISBN: 978-3-9524-2698-2 (electronic)ISBN: 978-1-5386-5303-6 (print)OAI: oai:DiVA.org:ltu-72550DiVA, id: diva2:1278595
Conference
European Control Conference, Cyprus, Limasson, 12-15 June, 2018
Available from: 2019-01-14 Created: 2019-01-14 Last updated: 2019-03-15

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Alhashimi, AnasVaragnolo, DamianoGustafsson, Thomas

Search in DiVA

By author/editor
Alhashimi, AnasVaragnolo, DamianoGustafsson, Thomas
By organisation
Signals and Systems
Control Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 37 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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