Details

Title Bayesian Estimation and Tracking: A Practical Guide
Author Haug, Anton J
Binding Hardback
Edition 1
Condition New
Pages 400
Volumes 1
Language ENG
Publisher Wiley
Publication date 2012-06-19
Illustrated Yes
Features Bibliography, Illustrated, Index, Table of Contents
Bookseller's Inventory # DADAX0470621702
ISBN 9780470621707 / 0470621702
Weight 1.5 lbs (0.68 kg)
Dimensions 9.3 x 6.1 x 0.9 in (23.62 x 15.49 x 2.29 cm)
Size 6.40x1.00x9.50
Category Mathematics
Library of Congress subjects Bayesian statistical decision theory, Estimation theory
Library of Congress Catalogue Number 2011044308
Dewey Decimal Code 519.542
Quantity available 1

About Ergodebooks Texas, United States

Specialising in: Rare Books, Rare Dvd, Rare Video, Textbook

Biblio member since 2005

Our goal is to provide best customer service and good condition books for the lowest possible price. We are always honest about condition of book. We list book only by ISBN # and hence exact book is guaranteed.

Terms of Sale:

We have 30 day return policy.

Browse books from Ergodebooks

Reader reviews for Bayesian Estimation and Tracking: A Practical Guide

Write a review for this book

Important Terms and Guidelines

Please focus on the book’s content and context. Also, add any personal comments as to how you enjoyed the book. Substantiate your likes and dislikes. You may make comparisons to other books.
Reviews must be at least 140 characters in length.
Please do not reveal critical plot elements.
This is not a help line. Contact customer support if you need help.

Your review must not include:

Obscenities, discriminatory language, or other insulting language not suitable for public domain
Advertisements, “spam” content, or references to other products, offers or websites.
Email addresses, URLs, phone numbers, physical addresses or other contact information.
Overly critical comments about other reviews or reviewers
Time-sensitive material (i.e. promotional tours, seminars, lectures, etc.)
Availability, price, or alternative ordering/shipping information

From the publisher

A practical approach to estimating and tracking dynamic systems in real-worl applications

Much of the literature on performing estimation for non-Gaussian systems is short on practical methodology, while Gaussian methods often lack a cohesive derivation. Bayesian Estimation and Tracking addresses the gap in the field on both accounts, providing readers with a comprehensive overview of methods for estimating both linear and nonlinear dynamic systems driven by Gaussian and non-Gaussian noices.

Featuring a unified approach to Bayesian estimation and tracking, the book emphasizes the derivation of all tracking algorithms within a Bayesian framework and describes effective numerical methods for evaluating density-weighted integrals, including linear and nonlinear Kalman filters for Gaussian-weighted integrals and particle filters for non-Gaussian cases. The author first emphasizes detailed derivations from first principles of eeach estimation method and goes on to use illustrative and detailed step-by-step instructions for each method that makes coding of the tracking filter simple and easy to understand.

Case studies are employed to showcase applications of the discussed topics. In addition, the book supplies block diagrams for each algorithm, allowing readers to develop their own MATLAB(R) toolbox of estimation methods.

Bayesian Estimation and Tracking is an excellent book for courses on estimation and tracking methods at the graduate level. The book also serves as a valuable reference for research scientists, mathematicians, and engineers seeking a deeper understanding of the topics.

From the rear cover

A practical approach to estimating and tracking dynamic systems in real-world applications

Much of the literature on performing estimation for non-Gaussian systems is short on practical methodology, while Gaussian methods often lack a cohesive derivation. Bayesian Estimation and Tracking addresses the gap in the field on both accounts, providing readers with a comprehensive overview of methods for estimating both linear and nonlinear dynamic systems driven by Gaussian and non-Gaussian noises.

Featuring a unified approach to Bayesian estimation and tracking, the book emphasizes the derivation of all tracking algorithms within a Bayesian framework and describes effective numerical methods for evaluating density-weighted integrals, including linear and nonlinear Kalman filters for Gaussian-weighted integrals and particle filters for non-Gaussian cases. The author first emphasizes detailed derivations from first principles of each estimation method and goes on to use illustrative and detailed step-by-step instructions for each method that makes coding of the tracking filter simple and easy to understand.

Case studies are employed to showcase applications of the discussed topics. In addition, the book supplies block diagrams for each algorithm, allowing readers to develop their own MATLAB(R) toolbox of estimation methods.

Bayesian Estimation and Tracking is an excellent book for courses on estimation and tracking methods at the graduate level. The book also serves as a valuable reference for research scientists, mathematicians, and engineers seeking a deeper understanding of the topics.

About the author

ANTON J. HAUG, PhD, is member of the technical staff at the Applied Physics Laboratory at The Johns Hopkins University, where he develops advanced target tracking methods in support of the Air and Missile Defense Department. Throughout his career, Dr. Haug has worked across diverse areas such as target tracking; signal and array processing and processor design; active and passive radar and sonar design; digital communications and coding theory; and time- frequency analysis.

BIBLIO is the largest independent book marketplace in the world, with over 100 million books.