LEADER 04155nam 2200661 a 450 001 9910829022603321 005 20200520144314.0 010 $a1-280-68723-1 010 $a9786613664174 010 $a1-118-28780-0 010 $a1-118-28783-5 010 $a1-118-28779-7 035 $a(CKB)3340000000001754 035 $a(EBL)837618 035 $a(SSID)ssj0000661062 035 $a(PQKBManifestationID)11401664 035 $a(PQKBTitleCode)TC0000661062 035 $a(PQKBWorkID)10710903 035 $a(PQKB)11044505 035 $a(Au-PeEL)EBL837618 035 $a(CaPaEBR)ebr10580296 035 $a(CaONFJC)MIL366417 035 $a(CaSebORM)9781118287804 035 $a(MiAaPQ)EBC837618 035 $a(OCoLC)798710560 035 $a(PPN)185060463 035 $a(EXLCZ)993340000000001754 100 $a20111201d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBayesian estimation and tracking$b[electronic resource] $ea practical guide /$fAnton J. Haug 205 $a1st edition 210 $aHoboken, N.J. $cWiley$d2012 215 $a1 online resource (397 p.) 300 $aDescription based upon print version of record. 311 $a0-470-62170-2 320 $aIncludes bibliographical references and index. 327 $apt. 1. Preliminaries -- pt. 2. The Gaussian assumption : a family of Kalman filter estimators -- pt. 3. Monte Carlo methods -- pt. 4. Additional case studies. 330 $a"This book presents a practical approach to estimation methods that are designed to provide a clear path to programming all algorithms. Readers are provided with a firm understanding of Bayesian estimation methods and their interrelatedness. Starting with fundamental principles of Bayesian theory, the book shows how each tracking filter is derived from a slight modification to a previous filter. Such a development gives readers a broader understanding of the hierarchy of Bayesian estimation and tracking. Following the discussions about each tracking filter, the filter is put into block diagram form for ease in future recall and reference. The book presents a completely unified approach to Bayesian estimation and tracking, and this is accomplished by showing that the current posterior density for a state vector can be linked to its previous posterior density through the use of Bayes' Law and the Chapman-Kolmogorov integral. Predictive point estimates are then shown to be density-weighted integrals of nonlinear functions. The book also presents a methodology that makes implementation of the estimation methods simple (or, rather, simpler than they have been in the past). Each algorithm is accompanied by a block diagram that illustrates how all parts of the tracking filter are linked in a never-ending chain, from initialization to the loss of track. These filter block diagrams provide a ready picture for implementing the algorithms into programmable code. In addition, four completely worked out case studies give readers examples of implementation, from simulation models that generate noisy observations to worked-out applications for all tracking algorithms. This book also presents the development and application of track performance metrics, including how to generate error ellipses when implementing in real-world applications, how to calculate RMS errors in simulation environments, and how to calculate Cramer-Rao lower bounds for the RMS errors. These are also illustrated in the case study presentations"--$cProvided by publisher. 606 $aBayesian statistical decision theory 606 $aAutomatic tracking$xMathematics 606 $aEstimation theory 615 0$aBayesian statistical decision theory. 615 0$aAutomatic tracking$xMathematics. 615 0$aEstimation theory. 676 $a519.5/42 686 $aMAT029010$2bisacsh 700 $aHaug$b Anton J.$f1941-$01596760 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910829022603321 996 $aBayesian estimation and tracking$93918261 997 $aUNINA