LAUSR

Abstract Nonlinear state estimation with delayed measurements has been considered in many industrial applications. However, classical methods cannot use these slow rates, irregular, delayed measurements, even though the delayed measurements are usually more accurate. Therefore, finding a method to utilize these delayed measurements can improve the accuracy and robustness of nonlinear state estimation. As this aim, one nonlinear state estimation method with delayed measurements using data fusion technique and cubature Kalman filter is proposed. The framework of processing delayed measurements was elaborated by applying the data fusion technique of covariance matrix. Then, two kinds of data fusion methods, with corresponding merits and faults in speed and accuracy, were described. Finally, the efficacy of the proposed methods is demonstrated by a chemical application of the nonlinear polymerization process.