Kalman filters for non-linear systems: a comparison of performance
By Tine Lefebvre *, Herman P. J. Bruyninckx, J. De Schutter. 2004
299 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/52f330ed3e3918d37760fb8f59da3de1137a4c64
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Robert J. R. Elliott
- Simon S. Haykin
- Aldo Ghisi
- Ienkaran Arasaratnam
- Karl Granström
- Marcus Baum
- Stefano Mariani
- S. Andrew Gadsden
In Artificial Intelligence
- Simon S. Haykin
- Ienkaran Arasaratnam
- Robert J. R. Elliott
- Aldo Ghisi
- Karl Granström
- Marcus Baum
- Stefano Mariani
- Simo Särkkä
In Statistics and Probability
- Robert J. R. Elliott
- Simon S. Haykin
- Aldo Ghisi
- Ienkaran Arasaratnam
- Karl Granström
- Marcus Baum
- Stefano Mariani
Citations in the corpus
- Extended Object Tracking: Introduction, Overview and Applications (2016, methodology): “More advanced methods are discussed in detail in [114].”
- Unscented Kalman filtering for nonlinear structural dynamics (2007, methodology): “Various alternative filtering procedures have been recently proposed to improve the EKF: surveys can be found, e.g., in [18, 34, 35 ].”
- Discrete-Time Nonlinear Filtering Algorithms Using Gauss–Hermite Quadrature (2007, background): “It has been shown that the measurement update step of the IKF is more accurate only when the measurement model fully observes the state, and this is rarely the case in practice [39].”
- On unscented Kalman filtering with state interval constraints (2010, unknown): “All rights reserved.”