In-process estimation of time-variant contingenently correlated measurands

This paper is devoted to the study and implementation of real-time techniques for the estimation of time-varying, contingently correlated quantities, and relevant uncertainty. An estimation algorithm based on a metrological customization of the Kalman filtering technique is presented, starting from a Bayesian approach. Moreover, a fuzzy-logic routine for real-time treatment of possible outliers is incorporated in the overall software procedure. The system applicability is demonstrated by results of simulations performed on dimensional measurement models.