The stability analysis of the adaptive fading extended Kalman filter using the innovation covariance

  • Kwang-Hoon Kim
  • Gyu-In Jee
  • Chan-Gook Park
  • Jang-Gyu Lee


The well-known conventional Kalman filter gives the optimal solution but to do so, it requires an accurate system model and exact stochastic information. However, in a number of practical situations, the system model and the stochastic information are incomplete. The Kalman filter with incomplete information may be degraded or even diverged. To solve this problem, a new adaptive fading filter using a forgetting factor has recently been proposed by Kim and co-authors. This paper analyzes the stability of the adaptive fading extended Kalman filter (AFEKF), which is a nonlinear filter form of the adaptive fading filter. The stability analysis of the AFEKF is based on the analysis result of Reif and co-authors for the EKF. From the analysis results, this paper shows the upper bounded condition of the error covariance for the filter stability and the bounded value of the estimation error. Keywords: Adaptive Kalman filter, forgetting factor, nonlinear filter, stability analysis.


Kalman Filter Extend Kalman Filter Measurement Equation Posteriori Estimation Error Nonlinear Stochastic System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© The Institute of Control, Robotics and Systems Engineers and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg GmbH 2009

Authors and Affiliations

  • Kwang-Hoon Kim
    • 1
  • Gyu-In Jee
    • 1
  • Chan-Gook Park
    • 2
  • Jang-Gyu Lee
    • 3
  1. 1.Department of Electronic EngineeringKonkuk UniversitySeoulKorea
  2. 2.School of Mechanical and Aerospace Engineering and the Institute of Advanced Aerospace TechnologySeoul National UniversitySeoulKorea
  3. 3.School of Electrical Engineering and Computer ScienceSeoul National UniversitySeoulKorea

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