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Extended Kalman Filter and System Identification

  • Charles K. ChuiEmail author
  • Guanrong Chen
Chapter

Abstract

The Kalman filtering process has been designed to estimate the state vector in a linear model. If the model turns out to be nonlinear, a linearization procedure is usually performed in deriving the filtering equations. We will consider a real-time linear Taylor approximation of the system function at the previous state estimate and that of the observation function at the corresponding predicted position.

Supplementary material

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of StatisticsStanford UniversityStanfordUSA
  2. 2.Department of Electronic EngineeringCity University of Hong KongHong KongChina

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