Eubank (mathematics and statistics, Arizona State U.) offers a self-contained, concise rigorous derivation of all the basic Kalman filter recursions from first principles. He lays out the basic prediction problem for signal-plus-noise models, deriving the Gramm-Schmidt algorithm and Cholesky decomposition. He covers the fundamental covariance struc.
"Eubank (mathematics and statistics, Arizona State U.) offers a self-contained, concise rigorous derivation of all the basic Kalman filter recursions from first principles. He lays out the basic prediction problem for signal-plus-noise models, deriving the Gramm-Schmidt algorithm and Cholesky decomposition. He covers the fundamental covariance struc."@en
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Kalman, Filtrage de - Manuels d'enseignement supérieur.
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MATHEMATICS Probability & Statistics Stochastic Processes.
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