.

0 (hodnocen0 x )

EB

ONLINE

Hoboken, N.J. : Wiley, 2012

1 online resource (xvii, 369 p.) : ill

ISBN 9781118287835 (electronic bk.)

ISBN 9780470621707 (hardback)

Includes bibliographical references and index

pt. 1. Preliminaries -- pt. 2. The Gaussian assumption : a family of Kalman filter estimators -- pt. 3. Monte Carlo methods -- pt. 4. Additional case studies.

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"-- Provided by publisher..

"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.-.

Electronic reproduction. Ann Arbor, MI : ProQuest, 2015. Available via World Wide Web. Access may be limited to ProQuest affiliated libraries

001737443

full

(Au-PeEL)EBL837618

(CaONFJC)MIL366417

(CaPaEBR)ebr10580296

(MiAaPQ)EBC837618

(OCoLC)794663337