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

Authors:
M. V. Kulikova
Affiliations:
School of Computational and Applied Mathematics, University of the Witwatersrand, Johannesburg, Republic of South Africa
Venue:
Automation and Remote Control
Year:
2009

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Abstract

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.