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

Journal article


Publication Details

Author(s): Garcia E, Hausotte T, Amthor A
Journal: Measurement
Publisher: Elsevier
Publication year: 2013
Volume: 46
Journal issue: 9
Pages range: 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 Authors / FAU Editors

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


External institutions with authors

Siemens AG, Sector Corporate Technology


How to cite

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: 

Last updated on 2019-05-06 at 14:34

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