Kalman Filtering - Theory and Practice Using MATLAB 4th

【实例简介】
KALMAN滤波 的经典著作，包含MATLB的处理，为英文版资料，相应的中文版也已出版发行
Copyright o 2015 by John Wiley Sons, Inc. 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The Math Works does not warrant the accuracy of the text or exercises in this book. This books use or discussion of MATLAB software or related products does not constitute endorsement or sponsorship by The Math Works of a particular pedagogical approach or particular use of the MATLAB( software Library of Congress Cataloging-in-Publication Data Grewal. Mohinder s Kalman filtering: theory and practice using MATLAB /Mohinder s Grewal, Angus P. Andrews Fourth edition pages cm Includes index ISBN978-1-118-851210( cloth) 1. Kalman filtering. 2. MATLAB. I. Andrews, Angus P. Il. TitI QA402.3.G6952015 629.8′312-dc23 2014020208 Printed in the united states of america 10987654321 CONTENTS Preface to the fourth edition Acknowledgements XIlI List of abbreviations 1 Introduction 1.1 Chapter Focus, 1.2 On Kalman Filtering, 1 1.3 On Optimal Estimation Methods, 6 1. 4 Common notation 28 5 Summary, 30 Problems. 31 References. 34 2 Linear Dynamic Systems 37 2.1 Chapter focus, 37 2.2 Deterministic Dynamic System Models, 42 2.3 Continuous Linear Systems and their solutions, 47 2.4 Discrete Linear Systems and their Solutions, 59 2.5 Observability of Linear Dynamic System Models, 61 2.6 Summary, 66 Problems. 69 References. 71 CONTENTS 3 Probability and Expectancy 73 3.1 Chapter focus, 73 3.2 Foundations of Probability Theory, 74 3.3 Expectancy, 79 3.4 Least-Mean-Square Estimate (MSE),87 3.5 Transformations of Variates. 93 3. 6 The Matrix Trace in Statistics. 102 3.7 Summary, 106 Problems. 107 References. 110 4 Random Processes 4.1 Chapter Focus, 111 4.2 Random Variables, Processes, and Sequences, 1 12 4.3 Statistical Properties, 114 4.4 Linear random process models 124 4.5 Shaping Filters(SF) and State Augmentation, 131 4.6 Mean and Covariance Propagation, 135 4.7 Relationships between Model Parameters, 145 4.8 Orthogonality Principle, 153 4.9 Summary, 157 Problems. 159 References. 167 5 Linear Optimal Filters and predictors 169 5.1 Chapter Focus, 169 5.2 Kalman Filter. 172 5.3 Kalman-Bucy Filter, 197 5.4 Optimal Linear predictors, 200 5.5 Correlated noise sources. 200 5.6 Relationships between Kalman and wiener Filters. 201 5.7 Quadratic Loss Functions, 202 5.8 Matrix Riccati Differential Equation, 204 5.9 Matrix Riccati Equation in Discrete Time, 219 5.10 Model Equations for Transformed State Variables, 223 5.11 Sample Applications, 224 5.12 Summary, 228 Problems. 232 References. 23.5 6 Optimal Smoothers 239 6. 1 Chapter Focus, 239 6.2 Fixed-Interval Smoothing, 244 6.3 Fixed-Lag Smoothing, 256 6. 4 Fixed-Point Smoothing, 268 CONTENTS vII 6.5 Summary, 275 Problems. 276 References. 278 7 Implementation Methods 281 7.1 Chapter Focus, 281 7.2 Computer roundoff, 283 7. 3 Effects of Roundoff Errors on Kalman Filters. 288 7.4 Factorization Methods for"Square-Root "Filtering, 294 7.5"Square-Root' and UD Filters, 318 7.6 Sigmarho Filtering, 330 7.7 Other Implementation Methods, 346 7.8 Summary, 358 Problems. 360 References. 363 8 Nonlinear Approximations 367 8.1 Chapter Focus, 367 8.2 The Affine Kalman filter. 370 8.3 Linear Approximations of Nonlinear Models, 372 8.4 Sample-and-Propagate Methods, 398 8.5 Unscented Kalman Filters (UKF),404 8.6 Truly nonlinear Estimation, 417 8.7 Summary, 419 Problems. 420 References. 423 9 Practical Considerations 427 9.1 Chapter Focus, 427 9.2 Diagnostic Statistics and Heuristics, 428 9.3 Prefiltering and Data Rejection Methods, 457 9. 4 Stability of Kalman Filters, 460 9.5 Suboptimal and Reduced-Order Filters, 461 9.6 Schmidt-Kalman Filtering, 471 9.8 Ways to Reduce Computational Requirements, 486.y 9.7 Memory, Throughput, and Wordlength Requirements, 478 9.9 Error Budgets and Sensitivity Analysis, 491 9. 10 Optimizing Measurement Selection Policies, 495 9.11 Summary, 501 Problems. 50 References. 502 0 Applications to Navigat 503 10.1 Chapter Focus, 503 10.2 Navigation Overview, 504 CONTENTS 10.3 Global Navigation Satellite Systems(GNSS), 510 10.4 Inertial Navigation Systems (INS), 544 10.5 GNSS/INS Integration, 578 10.6 Summary, 588 Problems. 590 References. 591 Appendix a Software 593 A 1 Appendix Focus, 593 A 2 Chapter 1 Software, 594 A 3 Chapter 2 Software, 594 A 4 Chapter 3 Software, 595 A. 5 Chapter 4 Software, 595 A6 Chapter 5 Software, 596 A7 Chapter 6 Software, 596 A 8 Chapter 7 Software, 597 A 9 Chapter 8 Software, 598 A 10 Chapter 9 Software, 599 A 11 Chapter 10 Software, 599 A. 1 2 Other Software Sources. 601 References. 603 Index 605 PREFACE TO THE FOURTH EDITION This book is designed to provide our readers a working familiarity with both the theoretical and practical aspects of Kalman filtering by including"real-world"prob lems in practice as illustrative examples. The material includes the essential technical background for Kalman filtering and the more practical aspects of implementation how to represent the problem in a mathematical model, analyze the performance of the estimator as a function of system design parameters, implement the mechanization equations in numerically stable algorithms, assess its computational requirements test the validity of results, and monitor the filter performance in operation. These are important attributes of the subject that are often overlooked in theoretical treatments but are necessary for application of the theory to real-world problems In this fourth edition . we have added a new chapter on the attributes of probabil ity distributions of importance in Kalman filtering, added two sections with easier derivations of the Kalman gain, added a section on a new sigmarho filter imple mentation, updated the treatment of nonlinear approximations to Kalman filtering, expanded coverage of applications in navigation, added many more derivations and implementations for satellite and inertial navigation error models, and included many new examples of sensor integration For readers who may need more background in matrix mathematics, we have included an Appendix b as a pdf file on the companion Wileywebsiteatwww.wiley.com/go/kalmanfiltering We have also updated the problem sets and incorporated helpful corrections and suggestions from our readers, reviewers, colleagues, and students for the overall improvement of the textbook All software has been provided in MATLAB, so that users can take advantage of its excellent graphing capabilities and a programming interface that is very close to the mathematical equations used for defining Kalman filtering and its applications The mAtLaB development environment also integrates with the Simulink simu lation environment for code verification on specific applications and code translation PREFACE TO THE FOURTH EDITION to C for the many applications microprocessors with C compilers. Appendix a has descriptions of the matlab software included on the companion Wiley web site The inclusion of the software is practically a matter of necessity, because Kalman filtering would not be very useful without computers to implement it. It is a bet- ter learning experience for the student to discover how the Kalman filter works by observing it in action The implementation of Kalman filtering on computers also illuminates some of the practical considerations of finite-wordlength arithmetic and the need for alterna- tive algorithms to preserve the accuracy of the results. If the student wishes to apply what she or he learns, then it is essential that she or he experience its workings and failings--and learn to recognize the difference The book is organized for use as a text for an introductory course in stochastic pro cesses at the senior level and as a first-year graduate-level course in Kalman filtering theory and application It could also be used for self-instruction or for purposes of review by practicing engineers and scientists who are not intimately familiar with the subject. Chapter I provides an informal introduction to the general subject matter by way of its history of development and application. Chapters 2-4 cover the essential background material on linear systems, probability, stochastic processes, and ran dom process modeling. These chapters could be covered in a senior-level course in electrical, computer, and systems engineering Chapter 5 covers linear optimal filters and predictors with derivations of the Kalman gain and detailed examples of applications. Chapter 6 is a tutorial-level treatment of optimal smoothing methods based on Kalman filtering models, includ ing more robust implementations. Chapter 7 covers the more recent implementation techniques for maintaining numerical accuracy, with algorithms provided for computer implementation Chapter 8 covers approximation methods used for nonlinear applications, includ g"extended"Kalman filters for"quasilinear"problems and tests for assessing hether extended Kalman filtering is adequate for the proposed application. We also present particle, sigma point, and the"unscented "Kalman filter implementation of Kalman filtering for problems failing the quasilinearity test. Applications of these techniques to the identification of unknown parameters of systems are given as examples. Chapter 9 deals with more practical matters of implementation and use beyond the numerical methods of Chapter 7. These matters include memory and throughput requirements(and methods to reduce them), divergence problems (and effective remedies ), and practical approaches to suboptimal filtering and measurement selection As a demonstration of how to develop and evaluate applications of Kalman filter ing, in Chapter 10, we show how to derive and implement different Kalman filtering configurations for Global Navigation Satellite System(GNSS)receivers and inertial navigation systems (INS)and for integrating GNSS receivers with INs Chapters 5-9 cover the essential material for a first-year graduate class in Kalman filtering theory and application or as a basic course in digital estimation theory and application. PREFACE TO THE FOURTH EDITION The organization of the material is illustrated by the following chapter-level depen dency graph, which shows how the subject of each chapter depends upon material in other chapters. The arrows in the figure indicate the recommended order of study Boxes above another box and connected by arrows indicate that the material repre sented by the upper boxes is background material for the subject in the lower box Dashed boxes indicate materials on the wiley companion web site B: a matrix refresher 1: Introduction 2: Linear dynamic systems 3: Probability distribution 4: Random processes 5: Linear optimal filters and predictors 6: Optimal smoothers 7: Implementation methods 8: nonlinear extension 9: Practical consideration 10: Application to navigation A: Software descriptions Matlab m-files PROF M S GREWAL. PHD. PE California State University at Fullerton ANGUS P ANDREWS. PHD Senior Scientist (ret), Rockwell Science Center, Thousand Oaks, California 【实例截图】
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