Process industries have variables that are measured at different rates, with some measurements, such as composition or quality variables, obtained after associated measurement delays. This work introduces a novel “sampled output augmentation” method for fusing delayed and infrequent primary measurements within a multirate Kalman filter (KF) framework. This is a state-augmentation-based method and is more parsimonious than other approaches. The stability of the sampled output augmentation is analyzed. We analytically show the equivalence of the proposed method with the traditional fixed-lag smoothing method for a linear system. Extension to nonlinear systems through extended Kalman filter (EKF) is presented. Finally, we assess the performance of the proposed method in comparison with other available estimators through linear and non-linear case studies.
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