Kalman Filtering: Theory and Practice Using MATLAB, 3rd EditionISBN: 9780470173664
592 pages
September 2008, WileyIEEE Press

Note: CDROM/DVD and other supplementary materials are not included as part of eBook file.
An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department  to obtain the manual, send an email to ialine@wiley.com.
Acknowledgments xiii
List of Abbreviations xv
1 General Information 1
1.1 On Kalman Filtering, 1
1.2 On Optimal Estimation Methods, 5
1.3 On the Notation Used In This Book, 23
1.4 Summary, 25
Problems, 26
2 Linear Dynamic Systems 31
2.1 Chapter Focus, 31
2.2 Dynamic System Models, 36
2.3 Continuous Linear Systems and Their Solutions, 40
2.4 Discrete Linear Systems and Their Solutions, 53
2.5 Observability of Linear Dynamic System Models, 55
2.6 Summary, 61
Problems, 64
3 Random Processes and Stochastic Systems 67
3.1 Chapter Focus, 67
3.2 Probability and Random Variables (RVs), 70
3.3 Statistical Properties of RVs, 78
3.4 Statistical Properties of Random Processes (RPs), 80
3.5 Linear RP Models, 88
3.6 Shaping Filters and State Augmentation, 95
3.7 Mean and Covariance Propagation, 99
3.8 Relationships Between Model Parameters, 105
3.9 Orthogonality Principle, 114
3.10 Summary, 118
Problems, 121
4 Linear Optimal Filters and Predictors 131
4.1 Chapter Focus, 131
4.2 Kalman Filter, 133
4.3 Kalman–Bucy Filter, 144
4.4 Optimal Linear Predictors, 146
4.5 Correlated Noise Sources, 147
4.6 Relationships Between Kalman–Bucy and Wiener Filters, 148
4.7 Quadratic Loss Functions, 149
4.8 Matrix Riccati Differential Equation, 151
4.9 Matrix Riccati Equation In Discrete Time, 165
4.10 Model Equations for Transformed State Variables, 170
4.11 Application of Kalman Filters, 172
4.12 Summary, 177
Problems, 179
5 Optimal Smoothers 183
5.1 Chapter Focus, 183
5.2 FixedInterval Smoothing, 189
5.3 FixedLag Smoothing, 200
5.4 FixedPoint Smoothing, 213
5.5 Summary, 220
Problems, 221
6 Implementation Methods 225
6.1 Chapter Focus, 225
6.2 Computer Roundoff, 227
6.3 Effects of Roundoff Errors on Kalman Filters, 232
6.4 Factorization Methods for SquareRoot Filtering, 238
6.5 SquareRoot and UD Filters, 261
6.6 Other Implementation Methods, 275
6.7 Summary, 288
Problems, 289
7 Nonlinear Filtering 293
7.1 Chapter Focus, 293
7.2 Quasilinear Filtering, 296
7.3 Sampling Methods for Nonlinear Filtering, 330
7.4 Summary, 345
Problems, 350
8 Practical Considerations 355
8.1 Chapter Focus, 355
8.2 Detecting and Correcting Anomalous Behavior, 356
8.3 Prefiltering and Data Rejection Methods, 379
8.4 Stability of Kalman Filters, 382
8.5 Suboptimal and ReducedOrder Filters, 383
8.6 Schmidt–Kalman Filtering, 393
8.7 Memory, Throughput, and Wordlength Requirements, 403
8.8 Ways to Reduce Computational Requirements, 409
8.9 Error Budgets and Sensitivity Analysis, 414
8.10 Optimizing Measurement Selection Policies, 419
8.11 Innovations Analysis, 424
8.12 Summary, 425
Problems, 426
9 Applications to Navigation 427
9.1 Chapter Focus, 427
9.2 Host Vehicle Dynamics, 431
9.3 Inertial Navigation Systems (INS), 435
9.4 Global Navigation Satellite Systems (GNSS), 465
9.5 Kalman Filters for GNSS, 470
9.6 Loosely Coupled GNSS/INS Integration, 488
9.7 Tightly Coupled GNSS/INS Integration, 491
9.8 Summary, 507
Problems, 508
Appendix A MATLAB Software 511
A.1 Notice, 511
A.2 General System Requirements, 511
A.3 CD Directory Structure, 512
A.4 MATLAB Software for Chapter 2, 512
A.5 MATLAB Software for Chapter 3, 512
A.6 MATLAB Software for Chapter 4, 512
A.7 MATLAB Software for Chapter 5, 513
A.8 MATLAB Software for Chapter 6, 513
A.9 MATLAB Software for Chapter 7, 514
A.10 MATLAB Software for Chapter 8, 515
A.11 MATLAB Software for Chapter 9, 515
A.12 Other Sources of Software, 516
Appendix B A Matrix Refresher 519
B.1 Matrix Forms, 519
B.2 Matrix Operations, 523
B.3 Block Matrix Formulas, 527
B.4 Functions of Square Matrices, 531
B.5 Norms, 538
B.6 Cholesky Decomposition, 541
B.7 Orthogonal Decompositions of Matrices, 543
B.8 Quadratic Forms, 545
B.9 Derivatives of Matrices, 546
Bibliography 549
Index 565
Mohinder S. Grewal, PhD, PE, is Professor of Electrical Engineering in the College of Engineering and Computer Science at California State University, Fullerton. He has more than thirtyfive years of experience in inertial navigation and control, and his mechanizations are currently used in commercial and military aircraft, surveillance satellites, missile and radar systems, freeway traffic control, and the Global Navigation Satellite System.
Angus P. Andrews, PhD, is a retired senior scientist from the Rockwell Science Center. His experience with aerospace systems analysis and design using Kalman filters began with his involvement in the Apollo moon project, and he is credited with the discovery of unknown landmark tracking as an orbital navigation method.
 Number of problems for third edition has been increased
 A new Chapter 5 on Optimal Smoothers has been added
 Old Chapter 5 now Chapter 6 on Nonlinear Filters with application has been fully revised
 "Application to Aided Inertial Navigation" from old Chapter 7 is now new Chapter 9 and has been greatly revised and expanded
 Appendix A contains additional new MATLAB software:
 for Chapter 3: conversion from continous process noise covariance to discrete sequence noise covariance
 for Chapter 4: sequential estimation (treating vector measurements as scalars for uncorrelated measurement covariance)
 for chapter 6: Unscented KF
 for chapter 9: Examples with 9, 11, 17 states with loosely and tightly coupled implementations
 This is the third edition of a proven textbook.

Book includes real world problems and solutions that the authors have developed over many years. Number of problems for third edition has been increased

Book is accompanied by MATLAB programs on Wiley ftp site.

Book will be accompanied by an Instructors Manual.

Appendix A contains additional new MATLAB software
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