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
Link To Document :
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