Covariance evaluation by means of uncertainty assessment

One of the key points in modern measurement science is the evaluation of measurement uncertainty, according to the definition given by the International Vocabulary of Metrology (VIM) [1] and the procedure recommended by the Guide to the Expression of Uncertainty in Measurement (GUM) and its supplements [2]-[4]. According to these documents, when several sources of uncertainty are present, the related standard uncertainties shall be quadratically combined, and the covariances must be considered, if present [2].

[1]  Alessandro Ferrero.  The pillars of metrology , 2015, IEEE Instrumentation & Measurement Magazine.

[2]  Gerd Vandersteen,et al.  Frequency Response Matrix Estimation From Missing Input–Output Data , 2015, IEEE Transactions on Instrumentation and Measurement.

[3]  Peter M. Harris,et al.  Principal Component Compression Method for Covariance Matrices Used for Uncertainty Propagation , 2015, IEEE Transactions on Instrumentation and Measurement.

[4]  C. Dubois.  Covariance dans le processus d’étalonnage , 2013 .

[5]  Mercede Bergoglio,et al.  Analysis of interlaboratory comparisons affected by correlations of the reference standards and drift of the travelling standards , 2011 .

[6]  Antonios Tsourdos,et al.  Robust Covariance Estimation for Data Fusion From Multiple Sensors , 2011, IEEE Transactions on Instrumentation and Measurement.

[7]  Alfredo Paolillo,et al.  Covariance Propagation for the Uncertainty Estimation in Stereo Vision , 2011, IEEE Transactions on Instrumentation and Measurement.

[8]  Dylan F. Williams,et al.  Traceable Waveform Calibration With a Covariance-Based Uncertainty Analysis , 2009, IEEE Transactions on Instrumentation and Measurement.