The basic parallel Kalman filtering algorithms derived by H.R. Hashemipour et al. (IEEE Trans. Autom. Control. vol.33, p.88-94, 1988) are summarized and generalized to the case of reduced-order local filters. Measurement-update and time-update equations are provided for four implementations: the conventional covariance filter, the conventional information filter, the square-foot covariance filter, and the square-foot information filter. A special feature of the suggested architecture is the ability to accommodate parallel local filters that have a smaller state dimension than the global filter. The estimates and covariance or information matrices (or their square roots) from these reduced-order filters are collated at a central filter at each step to generate the full-size, globally optimal estimates and their associated error covariance or information matrices (or their square roots). Aspects of computational complexity and the ensuing tradeoff with communication are discussed.<>
Square root parallel Kalman filtering using reduced-order local filters
IEEE Transactions on Aerospace and Electronic Systems ; 27 , 2 ; 276-289
01.03.1991
859307 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Square-root sigma-point Kalman filtering for spacecraft relative navigation
Online Contents | 2010
|Square-root quaternion cubature Kalman filtering for spacecraft attitude estimation
Online Contents | 2012
|Tidal flow forecasting using reduced rank square root filters
British Library Online Contents | 1997
|