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Adaptive robust extended Kalman filter for nonlinear stochastic systems

Adaptive robust extended Kalman filter for nonlinear stochastic systems

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  • Author(s): K. Xiong 1 ; H. Zhang 2 ; L. Liu 1
    • View affiliations
    • Affiliations: 1: National Laboratory of Space Intelligent Control, Beijing Institute of Control Engineering, Beijing, People's Republic of China
      2: School of Automation Science and Electrical Engineering, Beihang University, Beijing, People's Republic of China
  • Source: Volume 2, Issue 3, March 2008, p. 239 – 250
    DOI:  10.1049/iet-cta:20070096 , Print ISSN 1751-8644, Online ISSN 1751-8652
© The Institution of Engineering and Technology
Published

The authors analyse the error behaviour of the robust extended Kalman filter (REKF) for nonlinear stochastic systems. On the basis of some standard results about the boundedness of stochastic processes, it is specified that stability of the REKF cannot be guaranteed. In order to solve this problem, a novel method is proposed to design the REKF so that the sufficient conditions to ensure filter stability will be fulfilled. Furthermore, an adaptive scheme is adopted to automatically tune the error covariance matrix in response to the changing environment. Numerical example shows the superiority of the proposed adaptive REKF over the usual extended Kalman filter (EKF), the REKF and the adaptive EKF.

Inspec keywords: nonlinear filters; filtering theory; stochastic systems; Kalman filters; nonlinear control systems; covariance matrices; adaptive control; robust control

Other keywords: adaptive robust extended Kalman filter; error covariance matrix; filter stability; nonlinear stochastic systems; stochastic processes

Subjects: Nonlinear control systems; Time-varying control systems; Self-adjusting control systems; Stability in control theory; Algebra; Signal processing theory

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