New Proposal for Uncertainty Evaluation in Indirect Measurements

Abstract

This paper deals with the uncertainty evaluation in indirect measurements. The attention is mainly paid to measurement models whose input quantities are modeled as correlated random variates. Moving from a past proposal related to the use of the unscented transform to overcome key limitations of current guide to the expression of uncertainty in measurement recommendations in the presence of the input quantities modeled as uncorrelated random variates, the authors are going to present a new proposal capable of extending the advantages of the cited transform when correlated input random variates are also concerned. The new proposal is, in particular, addressed to nonlinear and/or nonanalytical measurement models. After describing the key features and implementation issues of the proposal, the results obtained in a number of tests on simulated and actual measurement data are given, which assess its reliability and effectiveness.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 26 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.