Kalman filtering approach to multi-rate information fusion in the presence of irregular sampling rate and variable measurement delay. (May 2017)

• Record Type: Journal Article
• Authors: Fatehi, Alireza; Huang, Biao
• Is Part Of: Journal of process control. Volume 53(2017)
• Page Start: 15
• Page End: 25
• Publication Date: 2017-05
• Subjects: ARSSE average root sum of squared error -- BFKF back calculation fused Kalman filter -- DP differential pressure -- DTTF delayed track to track fusion -- EIFKF exclusive information fusion Kalman filter -- FC fusion center -- FKFOE fused Kalman filter with output extrapolation -- FKF Frequent Kalman filter -- HRSG heat recovery steam generator -- IKF infrequent Kalman filter -- JFM just frequent measurement -- KF Kalman filter -- MDTTF modified delayed track to track fusion -- MDTTFR Riccati equation based MDTTF -- MTTF modified track to track fusion -- NTFKF negative time estimation fused Kalman filter -- OF outputs fusion -- PF parallel filter -- RMSE root mean square error -- SAGD steam assisted gravity drainage -- SF sequential filter -- TFP track fusion prediction -- TTF track to track fusion; Data fusion -- Irregular sampling -- Kalman filter -- Measurement delay -- Multi-rate measurement -- Oil sands industry; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2017.02.010
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: State estimation for systems with irregular rate and delayed measurements. Fusion of slow-rate high-quality data with fast-rate low-quality data to form more accurate fast-rate prediction of quality variables. A modified track to track Kalman filter fusion method is improved to handle infrequent, irregular and delayed measurements. Abstract: State estimation for a system with irregular rate and delayed measurements is studied using fusion Kalman filter. Lab data in process plants is usually more accurate compared to other measurements. However, it is often slow rate and subject to variable delay and irregularity in sampling time. Fast rate state estimation can be conducted using fast rate measurement, while the slow rate lab data can be used to improve the accuracy of estimation whenever it is available. For this purpose, two Kalman filters are used to estimate the states based on each type of measurement. The estimates are fused in the next step by considering the correlation between them. An iterative algorithm to obtain the cross-covariance matrix between the estimation errors of the two Kalman filters is presented and employed in the fusion process. The improvement on the accuracy of estimation and comparison with other optimal fusion state estimation techniques are discussed through a simulation example, a pilot-scale experiment and an industrial case study.