This book is dedicated to the memory of Gerald Bierman, Larry Levy, and James Vandergraft. They contributed much to the field, and are greatly missed.
DEDICATION
by Bruce P. Gibbs
Advanced Kalman Filtering, Least-Squares and Modeling: A Practical Handbook
- 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 LEAST-SQUARES ESTIMATION: FUNDAMENTALS
- CHAPTER 5 LINEAR LEAST-SQUARES ESTIMATION: SOLUTION TECHNIQUES
- 5.1 MATRIX NORMS, CONDITION NUMBER, OBSERVABILITY, AND THE PSEUDO-INVERSE
- 5.2 NORMAL EQUATION FORMATION AND SOLUTION
- 5.3 ORTHOGONAL TRANSFORMATIONS AND THE QR METHOD
- 5.4 LEAST-SQUARES SOLUTION USING THE SVD
- 5.5 ITERATIVE TECHNIQUES
- 5.6 COMPARISON OF METHODS
- 5.7 SOLUTION UNIQUENESS, OBSERVABILITY, AND CONDITION NUMBER
- 5.8 PSEUDO-INVERSES AND THE SINGULAR VALUE TRANSFORMATION (SVD)
- 5.9 SUMMARY
- CHAPTER 6 LEAST-SQUARES ESTIMATION: MODEL ERRORS AND MODEL ORDER
- CHAPTER 7 LEAST-SQUARES 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 (SQUARE-ROOT) 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: H-INFINITY 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
-
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