BAYES FILTER FOR IMPROVING AND FUSING DYNAMIC COORDINATE MEASUREMENTS

E. Garcia, T. Hausotte, A. Amthor

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 measurement uncertainty evaluation completely conform 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.

Keywords

dynamic coordinate measurements, Bayesian filtering, particle filter, Sequential Monte Carlo methods, online measurement uncertainty evaluation and data fusion

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IMEKO-WC-2012-TC21-O14.pdf

DOI

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IMEKO TC

TC21 - Mathematical Tools for Measurements

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