An algorithm for concurrent estimation of time-varying quantities

An original software system for in-process Bayesian estimation is presented with application to a vector of time-varying measurands. The estimation algorithm, mainly based on the Kalman filter technique, is an innovative application to in-process metrology. The programmed strategy, data flow, system/operator interfaces and implemented routines are illustrated and supported by numerical examples. The system performance is demonstrated by reporting and discussing results of simulation trials of metrological interest. The algorithm proves convergent even in severe trials.