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Extended Kalman Filters

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Encyclopedia of Systems and Control

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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.

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Bibliography

  • Crisan D, Rozovskii B (eds) (2011) The Oxford handbook of nonlinear filtering. Oxford University Press, Oxford/New York

    MATH  Google Scholar 

  • Daum FE, Fitzgerald RJ (1983) Decoupled Kalman filters for phased array radar tracking. IEEE Trans Autom Control 28:269–283

    Article  MATH  Google Scholar 

  • Gelb A et al (1974) Applied optimal estimation. MIT, Cambridge

    Google Scholar 

  • Markley FL, Crassidis JL, Cheng Y (2007) Nonlinear attitude filtering methods. AIAA J 30:12–28

    Google Scholar 

  • Mehra R (1971) A comparison of several nonlinear filters for reentry vehical tracking. IEEE Trans Autom Control 16:307–310

    Article  MathSciNet  Google Scholar 

  • Miller KS, Leskiw D (1982) Nonlinear observations with radar measurements. IEEE Trans Aerosp Electron Syst 2:192–200

    Article  Google Scholar 

  • Ristic B, Arulampalam S, Gordon N (2004) Beyond the Kalman filter. Artech House, Boston

    MATH  Google Scholar 

  • Schuster MD (1993) A survey of attitude representations. J Astronaut Sci 41:439–517

    MathSciNet  Google Scholar 

  • Sorenson H (ed) (1985) Kalman filtering: theory and application. IEEE, New York

    Google Scholar 

  • Stallard T (1991) Angle-only tracking filter in modified spherical coordinates. AIAA J Guid 14:694–696

    Article  Google Scholar 

  • Tanizaki H (1996) Nonlinear filters, 2nd edn. Springer, Berlin/New York

    Book  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Raytheon Company, 225 Presidential Way, 01801, Woburn, MA, USA

    Frederick E. Daum

Authors
  1. Frederick E. Daum
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Corresponding author

Correspondence to Frederick E. Daum .

Editor information

Editors and Affiliations

  1. Electrical and Computer Engineering, Boston University, Boston, Massachusetts, USA

    John Baillieul

  2. Automation and Control Solutions, Honeywell, Golden Valley, Minnesota, USA

    Tariq Samad

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© 2014 Springer-Verlag London

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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

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  • DOI: https://doi.org/10.1007/978-1-4471-5102-9_62-2

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  • Publisher Name: Springer, London

  • Online ISBN: 978-1-4471-5102-9

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