Critical Evaluation of Extended Kalman Filtering and Moving-Horizon Estimation

By Eric L. Haseltine, James B. Rawlings. 2005

451 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/eab02efb4409e1e5765618cfa155bbc69ac024c0

How researchers see this paper

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

In Artificial Intelligence

1. Lorenz T. Biegler

In Control and Systems Engineering

1. Victor M. Zavala
2. Lorenz T. Biegler
3. Moritz Diehl
4. Joel A. E. Andersson
5. Jaan-Willem Simon
6. D. Weichert

Citations in the corpus

• Applying the unscented Kalman filter for nonlinear state estimation (2008, unknown): “This way of the handling constraints in the EKF is termed as ‘clipping’ in literature [ 14 ].”
• Large-scale nonlinear programming using IPOPT: An integrating framework for enterprise-wide dynamic optimization (2009, background): “As shown recently in Rao, Rawlings, and Mayne (2003), MHE has desirable asymptotic stability properties and compares very well to extended Kalman filters (EKF) (Haseltine & Rawlings, 2005).”