À la Kalman Filtering for Metrology Tool With Application to Coordinate Measuring Machines

In-process treatment of measurement data is a challenging topic of industrial metrology. In the present research work, simultaneous estimation of measurand and related uncertainty is targeted from a measurement science standpoint. A metrological customization of the Kalman filter technique is implemented with application to basic coordinate measuring machine tasks. This is shown to accomplish Bayesian integration of the expert's preprocess knowledge and in-process measurement results-akin to type B and type A uncertainty evaluation in the framework of the Joint Committee for Guides in Metrology Guide to the expression of uncertainty in measurement, respectively. Uncertainty assessment is automated accordingly. Based on recursive processing of fresh data only, relevant advantages are real-time performance and memory saving.

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