On scalarized calculation of the likelihood function in array square-root filtering algorithms

An efficient method of scalarized calculation of the logarithmic likelihood function based on the array square-root implementation methods for Kalman filtering formulas was proposed. The algorithms of this kind were shown to be more stable to the roundoff errors than the conventional Kalman filter....

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Bibliographic Details
Published in:Automation and remote control Vol. 70; no. 5; pp. 855 - 871
Main Author: Kulikova, M. V.
Format: Journal Article
Language:English
Published: Dordrecht SP MAIK Nauka/Interperiodica 01.05.2009
Subjects:
CAE) and Design
Calculus of Variations and Optimal Control; Optimization
Computer-Aided Engineering (CAD
Control
Mathematics
Mathematics and Statistics
Mechanical Engineering
Mechatronics
Robotics
Stochastic Systems
Systems Theory
02.70.Rr
02.60.Dc
02.50.Ey
ISSN:0005-1179, 1608-3032
Online Access:Get full text
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Summary:An efficient method of scalarized calculation of the logarithmic likelihood function based on the array square-root implementation methods for Kalman filtering formulas was proposed. The algorithms of this kind were shown to be more stable to the roundoff errors than the conventional Kalman filter. The measurement scalarization technique enables a substantial reduction in the computational complexity of the algorithm. Additionally, the new implementations are classified with the array filtering algorithms and thereby are oriented to the parallel calculations. Computational results corroborated effectiveness of the new algorithm.
ISSN:0005-1179
1608-3032
DOI:10.1134/S0005117909050129