TOWARDS A GENERAL METROLOGY FOR DESIGNING SUB-NOISE MEASUREMENT PROCEDURES

R. Osegueda, G. R. Keller, S. A. Starks, R. Araiza, D. Bizyaev, V. Kreinovich

Abstract

In many practical situations, the measurement result <i>z</i> depends not only on the measured value <i>x</i>, but also on the parameters s describing the experiment’s setting and on the values of some auxiliary quantities <i>y</i>; the dependence <i>z</i> = <i>f</i>(<i>x</i>; <i>s</i>; <i>y</i>) of <i>z</i> on <i>x</i>, <i>s</i>, and <i>y</i> is usually known. In the ideal case when we know the exact value of the auxiliary parameter <i>y</i>, we can solve the above equation and find the desired value <i>x</i>. In many real-life situations, we only know y with some uncertainty, and this uncertainty leads to additional uncertainty in <i>x</i>.<BR />If we are trying to reconstruct <i>x</i> based on a <i>single</i> measurement result, then, of course, the measurement error in y leads to the corresponding measurement error in <i>x</i> – and, unless we perform more accurate measurements, we cannot improve <i>x</i>’s accuracy.<BR />In many practical situations, however, if we have <i>several</i> measurement results corresponding to different values of <i>t</i> and/or <i>y</i>, we can reconstruct <i>x</i> with a much higher accuracy – because we can combine these measurement results in such a way that the influence of <i>y</i> drastically decreases. As a result, we get a sub-noise measurement accuracy, the accuracy that is much better than the accuracy with which we know <i>y</i>.

Keywords

error correction, general measurement methodology

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DOI

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IMEKO TC

TC7 - Measurement Science

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