02386oam 2200601I 450 991079998060332120230828230916.00-429-11759-01-281-32617-897866113261731-4200-2867-710.1201/9781420028676(CKB)1000000000346551(EBL)263751(OCoLC)475983909(SSID)ssj0000186834(PQKBManifestationID)11166774(PQKBTitleCode)TC0000186834(PQKBWorkID)10252943(PQKB)11166685(MiAaPQ)EBC263751(Au-PeEL)EBL263751(CaPaEBR)ebr10143621(CaONFJC)MIL132617(OCoLC)437168728(OCoLC)123439160(EXLCZ)99100000000034655120180331d2006 uy 0engur|n|---|||||txtccrA Kalman filter primer /R.L. EubankBoca Raton, Fla. :Chapman & Hall/CRC,2006.1 online resource (199 p.)Statistics, textbooks and monographs ;v. 186Description based upon print version of record.0-8247-2365-1 Includes bibliographical references (p. 183-184) and index.chapter 1 Signal-Plus-Noise Models -- chapter 2 The Fundamental Covariance Structure -- chapter 3 Recursions for L and L−1 -- chapter 4 Forward Recursions -- chapter 5 Smoothing -- chapter 6 Initialization -- chapter 7 Normal Priors -- chapter 8 A General State-Space Model.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 strucStatistics, textbooks and monographs ;v. 186.Kalman filteringTextbooksKalman filtering519.2/3Eubank R. L(Randy L.),1586671FlBoTFGFlBoTFGBOOK9910799980603321A Kalman filter primer3873399UNINA