UNCERTAINTY ESTIMATION BASED ON INVERSE MODELS: AN OPEN QUESTION

Gabriele D’Antona

Abstract

In measurement science and its technological application most of the measurement methods are indirect. In order to measure the unknown physical quantity y we have to develop a forward model which relates this quantity to another quantity x directly measurable: x → y. Often the measurement model available is of opposite nature, i.e. y → x. It is thus necessary to invert the available model; this operation in some cases can lead to unacceptable level of uncertainty on the results. This paper shows the properties of some algorithms for processing measured data using ill posed inverse models employed for determining the distribution of indirectly measured quantities. The inversion procedure requires regularisation techniques in order to limit the uncertainty affecting the indirect measurements.

Keywords

uncertainty analysis, process tomography, signal processing, least squares methods

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IMEKO-TC4-2001-075.pdf

DOI

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IMEKO TC

TC4 - Measurement of Electrical Quantities

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