Bayes filter for dynamic coordinate measurements - Accuracy improvment, data fusion and measurement uncertainty evaluation

Beitrag in einer Fachzeitschrift


Details zur Publikation

Autor(en): Garcia E, Hausotte T, Amthor A
Zeitschrift: Measurement
Verlag: Elsevier
Jahr der Veröffentlichung: 2013
Band: 46
Heftnummer: 9
Seitenbereich: 3737-3744
ISSN: 0263-2241


Abstract

This paper presents a novel methodology to improve the measurement accuracy of dynamic measurements. This is achieved by deducing an online Bayes optimal estimate of the true measurand given uncertain, noisy or incomplete measurements within the framework of sequential Monte Carlo methods. The estimation problem is formulated as a general Bayesian inference problem for nonlinear dynamic systems. The optimal estimate is represented by probability density functions, which enable an online, probabilistic data fusion as well as a Bayesian measurement uncertainty evaluation corresponding to the "Guide to the expression of uncertainty in measurement". The efficiency and performance of the proposed methodology is verified and shown by dynamic coordinate measurements. © 2013 Elsevier Ltd. All rights reserved.


FAU-Autoren / FAU-Herausgeber

Garcia, Elmar
Lehrstuhl für Qualitätsmanagement und Fertigungsmeßtechnik
Hausotte, Tino Prof. Dr.-Ing.
Lehrstuhl für Fertigungsmesstechnik


Autor(en) der externen Einrichtung(en)
Siemens AG, Sector Corporate Technology


Zitierweisen

APA:
Garcia, E., Hausotte, T., & Amthor, A. (2013). Bayes filter for dynamic coordinate measurements - Accuracy improvment, data fusion and measurement uncertainty evaluation. Measurement, 46(9), 3737-3744. https://dx.doi.org/10.1016/j.measurement.2013.04.001

MLA:
Garcia, Elmar, Tino Hausotte, and Arvid Amthor. "Bayes filter for dynamic coordinate measurements - Accuracy improvment, data fusion and measurement uncertainty evaluation." Measurement 46.9 (2013): 3737-3744.

BibTeX: 

Zuletzt aktualisiert 2018-07-08 um 14:38