Advanced kalman filtering, least-squares and modeling, a practical handbook, Bruce P. Gibbs
Type
Label
Advanced kalman filtering, least-squares and modeling, a practical handbook, Bruce P. Gibbs
Language
eng
Bibliography note
Includes bibliographical references (pages 579-597) and index
Illustrations
illustrations
Index
index present
Literary Form
non fiction
Main title
Advanced kalman filtering, least-squares and modeling
Nature of contents
bibliographydictionaries
Oclc number
1705350791
Responsibility statement
Bruce P. Gibbs
Sub title
a practical handbook
Summary
This book is intended primarily as a handbook for engineers who must design practical systems. Its primarygoal is to discuss model development in sufficient detail so that the reader may design an estimator that meets all application requirements and is robust to modeling assumptions. Since it is sometimes difficult to ia priori/i determine the best model structure, use of iexploratory data analysis/i to define model structure is discussed. Methods for deciding on the "best" model are also presented./ A second goal is to present little known extensions of least squares estimation or Kalman filtering that provide guidance on model structure and parameters, or make the estimator more robust to changes in real-world behavior./ A third goal is discussion of implementation issues that make the estimator more accurate or efficient, or that make it flexible so that model alternatives can be easily compared./ The fourth goal is to provide the designer/analyst with guidance in evaluating estimator performance and in determining/correcting problems./ The final goal is to provide a subroutine library that simplifies implementation, and flexible general purpose high-level drivers that allow both easy analysis of alternative models and access to extensions of the basic filtering. / // Supplemental materials and up-to-date errata are downloadable at a href="http://booksupport.wiley.com/"http://booksupport.wiley.com/a
Table Of Contents
System Dynamics and Models -- Modeling Examples -- Linear Least-Squares Estimation: Fundamentals -- Linear Least-Squares Estimation: Solution Techniques -- Least-Squares Estimation: Model Errors and Model Order -- Least-Squares Estimation: Constraints, Nonlinear Models, and Robust Techniques -- Kalman Filtering -- Filtering for Nonlinear Systems, Smoothing, Error Analysis/Model Design, and Measurement Preprocessing -- Factored (Square-Root) Filtering -- Advanced Filtering Topics -- Empirical Modeling -- Appendix A: Summary of Vector/Matrix Operations -- Appendix B: Probability and Random Variables