Robust and reliable estimation via Unscented Recursive Nonlinear Dynamic Data Reconciliation

[1]  A. Jazwinski.  Stochastic Processes and Filtering Theory , 1970 .

[2]  Arthur Gelb,et al.  Applied Optimal Estimation , 1974 .

[3]  T. Edgar,et al.  A sequential error-in-variables method for nonlinear dynamic systems , 1991 .

[4]  Masoud Soroush,et al.  Nonlinear State Estimation in a Polymerization Reactor , 1997 .

[5]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[6]  Hugh F. Durrant-Whyte,et al.  A new method for the nonlinear transformation of means and covariances in filters and estimators , 2000, IEEE Trans. Autom. Control..

[7]  V. P. Barbosa,et al.  Development of data reconciliation for dynamic nonlinear system: application the polymerization reactor , 2000 .

[8]  Masoud Soroush,et al.  Real‐time multirate state estimation in a pilot‐scale polymerization reactor , 2002 .

[9]  David Q. Mayne,et al.  Constrained state estimation for nonlinear discrete-time systems: stability and moving horizon approximations , 2003, IEEE Trans. Autom. Control..

[10]  Andrei Romanenko,et al.  The unscented filter as an alternative to the EKF for nonlinear state estimation: a simulation case study , 2004, Comput. Chem. Eng..

[11]  B. Bakshi,et al.  Bayesian estimation via sequential Monte Carlo sampling: Unconstrained nonlinear dynamic systems , 2004 .

[12]  Paulo Afonso,et al.  Unscented Kalman Filtering of a Simulated pH System , 2004 .

[13]  James B. Rawlings,et al.  Critical Evaluation of Extended Kalman Filtering and Moving-Horizon Estimation , 2005 .

[14]  Raghunathan Rengaswamy,et al.  Recursive estimation in constrained nonlinear dynamical systems , 2005 .