Bayesian strategies for calibrating heteroskedastic static sensors with unknown model structuresShow others and affiliations
2018 (English)In: 2018 European Control Conference (ECC), 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
IEEE, 2018. p. 2447-2453
National Category
Control Engineering Probability Theory and Statistics
Research subject
Control Engineering
Identifiers
URN: urn:nbn:se:oru:diva-82176DOI: 10.23919/ECC.2018.8550201ISI: 000467725302079Scopus ID: 2-s2.0-85059819837ISBN: 978-3-9524-2698-2 (electronic)ISBN: 978-1-5386-5303-6 (print)OAI: oai:DiVA.org:oru-82176DiVA, id: diva2:1433182
Conference
European Control Conference (ECC), Limassol, Cyprus, June 12-15, 2018.
Note
Funding Agency:
Norrbottens Forskningsrad
University of Baghdad
2020-05-292020-05-292020-08-19Bibliographically approved