On Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear Systems

By Simo Särkkä. IEEE Transactions on Automatic Control, 2007

460 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Cubature Kalman Filtering for Continuous-Discrete Systems: Theory and Simulations (2010, methodology): “Then, the results are numerically integrated up to time ; the Runge-Kutta method is used as a numerical integration tool in [38].”
Approximate robust optimization of nonlinear systems under parametric uncertainty and process noise (2015, methodology): “Although the EKF is widely known, applications of the variance prediction in view of robust control are still scarce (Särkkä, 2007).”
Reduced-order Unscented Kalman Filtering with application to parameter identification in large-dimensional systems (2011, methodology): “Nevertheless, a continuous-time extension was proposed in [21] based on a formal limiting procedure with respect to the time-step as is classical in Kalman theory.”
Applied Stochastic Differential Equations (2019, unknown): “For example, by selecting the unscented transform sigma points and weights we get the unscented Kalman–Bucy filter (Särkkä, 2007).”