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
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