Book description
This book provides a complete explanation of estimation theory and application, modeling approaches, and model evaluation. Each topic starts with a clear explanation of the theory (often including historical context), followed by application issues that should be considered in the design. Different implementations designed to address specific problems are presented, and numerous examples of varying complexity are used to demonstrate the concepts.
This book is intended primarily as a handbook for engineers who must design practical systems. Its primary goal is to explain all important aspects of Kalman filtering and leastsquares theory and application. Discussion of estimator design and model development is emphasized so that the reader may develop an estimator that meets all application requirements and is robust to modeling assumptions. Since it is sometimes difficult to a priori determine the best model structure, use of exploratory data analysis 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 realworld 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 highlevel drivers that allow both easy analysis of alternative models and access to extensions of the basic filtering.
Table of contents
 Cover
 About the Cover
 Title page
 Copyright page
 DEDICATION
 PREFACE
 CHAPTER 1 INTRODUCTION
 CHAPTER 2 SYSTEM DYNAMICS AND MODELS
 CHAPTER 3 MODELING EXAMPLES
 CHAPTER 4 LINEAR LEASTSQUARES ESTIMATION: FUNDAMENTALS

CHAPTER 5 LINEAR LEASTSQUARES ESTIMATION: SOLUTION TECHNIQUES
 5.1 MATRIX NORMS, CONDITION NUMBER, OBSERVABILITY, AND THE PSEUDOINVERSE
 5.2 NORMAL EQUATION FORMATION AND SOLUTION
 5.3 ORTHOGONAL TRANSFORMATIONS AND THE QR METHOD
 5.4 LEASTSQUARES SOLUTION USING THE SVD
 5.5 ITERATIVE TECHNIQUES
 5.6 COMPARISON OF METHODS
 5.7 SOLUTION UNIQUENESS, OBSERVABILITY, AND CONDITION NUMBER
 5.8 PSEUDOINVERSES AND THE SINGULAR VALUE TRANSFORMATION (SVD)
 5.9 SUMMARY
 CHAPTER 6 LEASTSQUARES ESTIMATION: MODEL ERRORS AND MODEL ORDER
 CHAPTER 7 LEASTSQUARES ESTIMATION: CONSTRAINTS, NONLINEAR MODELS, AND ROBUST TECHNIQUES
 CHAPTER 8 KALMAN FILTERING
 CHAPTER 9 FILTERING FOR NONLINEAR SYSTEMS, SMOOTHING, ERROR ANALYSIS/MODEL DESIGN, AND MEASUREMENT PREPROCESSING
 CHAPTER 10 FACTORED (SQUAREROOT) FILTERING

CHAPTER 11 ADVANCED FILTERING TOPICS
 11.1 MAXIMUM LIKELIHOOD PARAMETER ESTIMATION
 11.2 ADAPTIVE FILTERING
 11.3 JUMP DETECTION AND ESTIMATION
 11.4 ADAPTIVE TARGET TRACKING USING MULTIPLE MODEL HYPOTHESES
 11.5 CONSTRAINED ESTIMATION
 11.6 ROBUST ESTIMATION: HINFINITY FILTERS
 11.7 UNSCENTED KALMAN FILTER (UKF)
 11.8 PARTICLE FILTERS
 11.9 SUMMARY
 CHAPTER 12 EMPIRICAL MODELING
 APPENDIX A SUMMARY OF VECTOR/MATRIX OPERATIONS
 APPENDIX B PROBABILITY AND RANDOM VARIABLES
 BIBLIOGRAPHY
 Index
Product information
 Title: Advanced Kalman Filtering, LeastSquares and Modeling: A Practical Handbook
 Author(s):
 Release date: March 2011
 Publisher(s): Wiley
 ISBN: 9780470529706
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