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عنوان
Bayesian Estimation and Tracking

پدید آورنده
/ Anton J. Haug

موضوع
Bayesian statistical decision theory,Automatic tracking -- Mathematics,Estimation theory,نظریه تصمیم گیری آماری بیزی,ردگیری اتوماتیک - ریاضیات,نظریه تخمین

رده
QA279
,.
5
.
H38

کتابخانه
Library of Foreign Languages and Islamic Sources

محل استقرار
استان: Qom ـ شهر: Qom

Library of Foreign Languages and Islamic Sources

تماس با کتابخانه : 37839111

NATIONAL BIBLIOGRAPHY NUMBER

Country Code
IR
Number
989604

LANGUAGE OF THE ITEM

.Language of Text, Soundtrack etc
انگلیسی

COUNTRY OF PUBLICATION OR PRODUCTlON

Country of publication
IR

TITLE AND STATEMENT OF RESPONSIBILITY

Title Proper
Bayesian Estimation and Tracking
General Material Designation
[Book]
Other Title Information
:A Practical Guide
First Statement of Responsibility
/ Anton J. Haug

.PUBLICATION, DISTRIBUTION, ETC

Place of Publication, Distribution, etc.
Hoboken, N.J.
Name of Publisher, Distributor, etc.
: Wiley
Date of Publication, Distribution, etc.
, 2012.

PHYSICAL DESCRIPTION

Specific Material Designation and Extent of Item
xxvi, 369 p ; ill.

NOTES PERTAINING TO PUBLICATION, DISTRIBUTION, ETC.

Text of Note
Print

INTERNAL BIBLIOGRAPHIES/INDEXES NOTE

Text of Note
Bibliography

EXTERNAL INDEXES/ABSTRACTS/REFERENCES NOTE

Name of source
Index

CONTENTS NOTE

Text of Note
"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"--Provided by publisher.

TOPICAL NAME USED AS SUBJECT

Bayesian statistical decision theory
Automatic tracking -- Mathematics
Estimation theory
نظریه تصمیم گیری آماری بیزی
ردگیری اتوماتیک - ریاضیات
نظریه تخمین

LIBRARY OF CONGRESS CLASSIFICATION

Class number
QA279
,.
5
Book number
.
H38

PERSONAL NAME - PRIMARY RESPONSIBILITY

Haug, Anton J., 1941-

ORIGINATING SOURCE

Country
ایران
Agency
Library of Foreign Languages and Islamic resource

old catalog

p

BL
1

a
Y

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