Bayesian Estimation and Tracking
eBook - ePub

Bayesian Estimation and Tracking

A Practical Guide

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Bayesian Estimation and Tracking

A Practical Guide

About this book

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® 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.

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Yes, you can access Bayesian Estimation and Tracking by Anton J. Haug in PDF and/or ePUB format, as well as other popular books in Matematica & Probabilità e statistica. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Wiley
Year
2012
Print ISBN
9780470621707
eBook ISBN
9781118287804

Table of contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Dedication
  5. Preface
  6. Acknowledgments
  7. List of Figures
  8. List of Tables
  9. Part I: Preliminaries
  10. Part II: The Gaussian Assumption: A Family of Kalman Filter Estimators
  11. Part III: Monte Carlo Methods
  12. Part IV: Additional Case Studies
  13. Index
  14. Wiley End User License Agreement