• DocumentCode
    1222502
  • Title

    Square-Root Quadrature Kalman Filtering

  • Author

    Arasaratnam, Ienkaran ; Haykin, Simon

  • Author_Institution
    Cognitive Syst. Lab., McMaster Univ., Hamilton, ON
  • Volume
    56
  • Issue
    6
  • fYear
    2008
  • fDate
    6/1/2008 12:00:00 AM
  • Firstpage
    2589
  • Lastpage
    2593
  • Abstract
    The quadrature Kalman filter (QKF) is a recursive, nonlinear filtering algorithm developed in the Kalman filtering framework. It computes the mean and covariance of all conditional densities using the Gauss-Hermite quadrature rule. In this correspondence, we develop a square-root extension of the quadrature Kalman filter using matrix triangularizations. The square-root quadrature Kalman filter (SQKF) propagates the mean and the square root of the covariance. Although equivalent to the QKF algebraically, the SQKF exhibits excellent numerical characteristics, but at the expense of increased computational complexity. We also present possible refinements of the generic SQKF.
  • Keywords
    Hermitian matrices; Kalman filters; covariance matrices; Gauss-Hermite quadrature rule; covariance; matrix triangularization; nonlinear filtering algorithm; square-root quadrature kalman filter; Arithmetic; Bayesian methods; Computational complexity; Covariance matrix; Filtering algorithms; Gaussian processes; Kalman filters; Nonlinear dynamical systems; Nonlinear systems; State estimation; Matrix triangularization; quadrature Kalman filter (QKF); quadrature rule; square-root filter;
  • fLanguage
    English
  • Journal_Title
    Signal Processing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1053-587X
  • Type

    jour

  • DOI
    10.1109/TSP.2007.914964
  • Filename
    4524036