Kalman filtering with state equality constraints

By D. Simon, T. Chia. 2002

644 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/88149cbeec003bb41d986154b3214d9de82285c6

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In Control and Systems Engineering

Citations in the corpus

Data Assimilation for Estimating the Terrestrial Water Budget Using a Constrained Ensemble Kalman Filter (2006, methodology): “two-step or sequential filtering), where a second filter is constructed to satisfy the constraints (Simon and Chia, 2002).”
Kalman filtering with state constraints: a survey of linear and nonlinear algorithms (2010, methodology): “However, if Q satisfies DQDT = 0 then the standard Kalman filter estimate satisfies the state constraint Dx̂+k = 0, and the system projection filter, the estimate projection filter, and the standard Kalman filter are all identical.”
Kalman filtering with inequality constraints for turbofan engine health estimation (2006, methodology): “It is based on a generalization of the approach presented in [2], which dealt with the incorporation of state variable equality constraints in the Kalman ̄lter.”
Distributed recursive filtering for stochastic systems under uniform quantizations and deception attacks through sensor networks (2017, background): “Examples include, but are not limited to, the robust KF for uncertain systems [12], the extended KF (EKF) or the unscented KF for nonlinear systems [15] and the KF/EKF for systems with equality constraints [29].”
Applying the unscented Kalman filter for nonlinear state estimation (2008, background): “However, the constraints can be incorporated in the KF by projecting the unconstrained KF estimates onto the boundary of the feasible region at each time step [18,19].”
Robust Kalman filtering with constraints: a case study for integrated navigation (2010, methodology): “There are several approaches to the constrained Kalman filtering given in the literatures (Simon and Chia 2002; Simon and Simon 2004), for example, the maximum probability method, the mean squared error method and the projection method ( Simon and Chia 2002 ).”
On Kalman Filtering With Nonlinear Equality Constraints (2007, methodology): “2Exactly the same approach was subsequently proposed by Simon [8].”
Parameter Estimation and Model Selection in Computational Biology (2010, methodology): “To cope with these issues, we apply the constrained estimation technique developed in [30,31].”
On unscented Kalman filtering with state interval constraints (2010, methodology): “Model information is used during the forecast step, while measurement data are injected into the estimates during the data-assimilation step.”
Ltd Registered in England and Wales Registered Number : 1072954 Registered office : (2004, positive): “In addition, a land-based vehicle linear example with kinematic constraints (Simon and Chia 2002; Ko and Bitmead 2007) is investigated using KF, ECKF, MAKF, PKF-SP and PKF-EP in Teixeira (2008, Chapter 5).”
State and parameter estimation of hydrologic models using the constrained ensemble Kalman filter (2009, methodology): “…by substituting equation (20) into equation (19): ~x ¼ x̂ AT AAT 1 A x̂ bð Þ ð21Þ The solution of equation (16) is the same as projecting the unconstrained KF solution to the constrained surface [Simon and Chia, 2002]: min ~x x̂ð ÞT ~x x̂ð Þ s:t: A~x ¼ b ð22Þ Thus the obtained ~x by the…”
Survey of maneuvering target tracking. Part V. Multiple-model methods (2005, methodology): “Embedding map information was made by incorporating an elegant kinematic constraint technique [314] (e.”