Abstract
The extended Kalman filter (EKF) is the most popular estimation algorithm in practical applications. It is based on a linear approximation to the Kalman filter theory. There are thousands of variations of the basic EKF design, which are intended to mitigate the effects of nonlinearities, non-Gaussian errors, ill-conditioning of the covariance matrix and uncertainty in the parameters of the problem.
Similar content being viewed by others
Bibliography
Crisan D, Rozovskii B (eds) (2011) The Oxford handbook of nonlinear filtering. Oxford University Press, Oxford/New York
Daum FE, Fitzgerald RJ (1983) Decoupled Kalman filters for phased array radar tracking. IEEE Trans Autom Control 28:269–283
Gelb A et al (1974) Applied optimal estimation. MIT, Cambridge
Markley FL, Crassidis JL, Cheng Y (2007) Nonlinear attitude filtering methods. AIAA J 30:12–28
Mehra R (1971) A comparison of several nonlinear filters for reentry vehical tracking. IEEE Trans Autom Control 16:307–310
Miller KS, Leskiw D (1982) Nonlinear observations with radar measurements. IEEE Trans Aerosp Electron Syst 2:192–200
Ristic B, Arulampalam S, Gordon N (2004) Beyond the Kalman filter. Artech House, Boston
Schuster MD (1993) A survey of attitude representations. J Astronaut Sci 41:439–517
Sorenson H (ed) (1985) Kalman filtering: theory and application. IEEE, New York
Stallard T (1991) Angle-only tracking filter in modified spherical coordinates. AIAA J Guid 14:694–696
Tanizaki H (1996) Nonlinear filters, 2nd edn. Springer, Berlin/New York
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag London
About this entry
Cite this entry
Daum, F.E. (2014). Extended Kalman Filters. In: Baillieul, J., Samad, T. (eds) Encyclopedia of Systems and Control. Springer, London. https://doi.org/10.1007/978-1-4471-5102-9_62-2
Download citation
DOI: https://doi.org/10.1007/978-1-4471-5102-9_62-2
Received:
Accepted:
Published:
Publisher Name: Springer, London
Online ISBN: 978-1-4471-5102-9
eBook Packages: Springer Reference EngineeringReference Module Computer Science and Engineering