
Applied State Estimation and Association
MIT Press
Published on 15. August 2023
Book
Paperback/Softback
472 pages
978-0-262-54891-5 (ISBN)
Article exhausted; check different version
Description
A rigorous introduction to the theory and applications of state estimation and association, an important area in aerospace, electronics, and defense industries.
Applied state estimation and association is an important area for practicing engineers in aerospace, electronics, and defense industries, used in such tasks as signal processing, tracking, and navigation. This book offers a rigorous introduction to both theory and application of state estimation and association. It takes a unified approach to problem formulation and solution development that helps students and junior engineers build a sound theoretical foundation for their work and develop skills and tools for practical applications.
Chapters 1 through 6 focus on solving the problem of estimation with a single sensor observing a single object, and cover such topics as parameter estimation, state estimation for linear and nonlinear systems, and multiple model estimation algorithms. Chapters 7 through 10 expand the discussion to consider multiple sensors and multiple objects. The book can be used in a first-year graduate course in control or system engineering or as a reference for professionals. Each chapter ends with problems that will help readers to develop derivation skills that can be applied to new problems and to build computer models that offer a useful set of tools for problem solving. Readers must be familiar with state-variable representation of systems and basic probability theory including random and stochastic processes.
Applied state estimation and association is an important area for practicing engineers in aerospace, electronics, and defense industries, used in such tasks as signal processing, tracking, and navigation. This book offers a rigorous introduction to both theory and application of state estimation and association. It takes a unified approach to problem formulation and solution development that helps students and junior engineers build a sound theoretical foundation for their work and develop skills and tools for practical applications.
Chapters 1 through 6 focus on solving the problem of estimation with a single sensor observing a single object, and cover such topics as parameter estimation, state estimation for linear and nonlinear systems, and multiple model estimation algorithms. Chapters 7 through 10 expand the discussion to consider multiple sensors and multiple objects. The book can be used in a first-year graduate course in control or system engineering or as a reference for professionals. Each chapter ends with problems that will help readers to develop derivation skills that can be applied to new problems and to build computer models that offer a useful set of tools for problem solving. Readers must be familiar with state-variable representation of systems and basic probability theory including random and stochastic processes.
More details
Series
Language
English
Place of publication
Cambridge (Massachusetts)
United States
Publishing group
MIT Press Ltd
Target group
College/higher education
Professional and scholarly
Interest Age: From 18 years
Product notice
Paperback (trade)
Illustrations
42 COLOR ILLUS., 66 B&W ILLUS.
Dimensions
Height: 229 mm
Width: 178 mm
Thickness: 30 mm
Weight
1184 gr
ISBN-13
978-0-262-54891-5 (9780262548915)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Chaw-Bing Chang | Keh-Ping Dunn
Applied State Estimation and Association
E-Book
07/2016
MIT Press
€108.99
Available for download
Persons
Chaw-Bing Chang is a Senior Staff Member of the BMDS (Ballistic Missile Defense System) Integration Group at MIT Lincoln Laboratory. Chang has forty years of experience in applying estimation and association techniques.
Keh-Ping Dunn is a Senior Staff Member of the BMDS Integration Group at MIT Lincoln Laboratory. Dunn has forty years of experience in applying estimation and association techniques.
Keh-Ping Dunn is a Senior Staff Member of the BMDS Integration Group at MIT Lincoln Laboratory. Dunn has forty years of experience in applying estimation and association techniques.
Author
Senior StaffMIT Lincoln Laboratory
Senior StaffMIT Lincoln Laboratory
Content
Preface xvii
About the Authors xix
Acknowledgments xxi
Introduction xxiii
1 Parameter Estimation 1
2 State Estimation for Linear Systems 51
3 State Estimation for Nonlinear Systems 99
4 Practical Considerations in Kalman Filter Design 141
5 Multiple Model Estimation Algorithms 197
6 Sampling Techniques for State Estimations 227
7 State Estimation with Multiple Sensor Systems 271
8 Estimation and Association with Uncertain Measurement Origin 303
9 Multiple Hypothesis Tracking Algorithm 257
10 Multiple Sensor Correlation and Fusion with Biased Measurements 391
Concluding Remarks 413
Appendix A Matrix Inversion Lemma 417
Appendix B Notation and Variables 419
Appendix C Definition of Terminology Used in Tracking 425
Index 431
About the Authors xix
Acknowledgments xxi
Introduction xxiii
1 Parameter Estimation 1
2 State Estimation for Linear Systems 51
3 State Estimation for Nonlinear Systems 99
4 Practical Considerations in Kalman Filter Design 141
5 Multiple Model Estimation Algorithms 197
6 Sampling Techniques for State Estimations 227
7 State Estimation with Multiple Sensor Systems 271
8 Estimation and Association with Uncertain Measurement Origin 303
9 Multiple Hypothesis Tracking Algorithm 257
10 Multiple Sensor Correlation and Fusion with Biased Measurements 391
Concluding Remarks 413
Appendix A Matrix Inversion Lemma 417
Appendix B Notation and Variables 419
Appendix C Definition of Terminology Used in Tracking 425
Index 431