{"product_id":"kalman-filtering-9781118851210","title":"Kalman Filtering","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eThe definitive textbook and professional reference on Kalman Filtering  fully updated, revised, and expanded\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis book contains the latest developments in the implementation and application of Kalman filtering. Authors Grewal and Andrews draw upon their decades of experience to offer an in-depth examination of the subtleties, common pitfalls, and limitations of estimation theory as it applies to real-world situations. They present many illustrative examples including adaptations for nonlinear filtering, global navigation satellite systems, the error modeling of gyros and accelerometers, inertial navigation systems, and freeway traffic control.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eKalman Filtering: Theory and Practice Using MATLAB, Fourth Edition\u003c\/i\u003e is an ideal textbook in advanced undergraduate and beginning graduate courses in stochastic processes and Kalman filtering. It is also appropriate for self-instruction or review by practicing engineers and scientists who want to learn more about this\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"The book \"Kalman Filtering: Theory and practice with MATLAB\" is a well-written text with modern ideas which are expressed in a rigorous and clear manner. It is also a professional reference on Kalman filtering: fully updated, revised, and expanded.\" (Zentralblatt MATH 2016)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003ePreface to the Fourth Edition ix\u003c\/p\u003e \u003cp\u003eAcknowledgements xiii\u003c\/p\u003e \u003cp\u003eList of Abbreviations xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Chapter Focus 1\u003c\/p\u003e \u003cp\u003e1.2 On Kalman Filtering 1\u003c\/p\u003e \u003cp\u003e1.3 On Optimal Estimation Methods 6\u003c\/p\u003e \u003cp\u003e1.4 Common Notation 28\u003c\/p\u003e \u003cp\u003e1.5 Summary 30\u003c\/p\u003e \u003cp\u003eProblems 31\u003c\/p\u003e \u003cp\u003eReferences 34\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Linear Dynamic Systems 37\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Chapter Focus 37\u003c\/p\u003e \u003cp\u003e2.2 Deterministic Dynamic System Models 42\u003c\/p\u003e \u003cp\u003e2.3 Continuous Linear Systems and their Solutions 47\u003c\/p\u003e \u003cp\u003e2.4 Discrete Linear Systems and their Solutions 59\u003c\/p\u003e \u003cp\u003e2.5 Observability of Linear Dynamic System Models 61\u003c\/p\u003e \u003cp\u003e2.6 Summary 66\u003c\/p\u003e \u003cp\u003eProblems 69\u003c\/p\u003e \u003cp\u003eReferences 71\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Probability and Expectancy 73\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Chapter Focus 73\u003c\/p\u003e \u003cp\u003e3.2 Foundations of Probability Theory 74\u003c\/p\u003e \u003cp\u003e3.3 Expectancy 79\u003c\/p\u003e \u003cp\u003e3.4 Least-Mean-Square Estimate (LMSE) 87\u003c\/p\u003e \u003cp\u003e3.5 Transformations of Variates 93\u003c\/p\u003e \u003cp\u003e3.6 The Matrix Trace in Statistics 102\u003c\/p\u003e \u003cp\u003e3.7 Summary 106\u003c\/p\u003e \u003cp\u003eProblems 107\u003c\/p\u003e \u003cp\u003eReferences 110\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Random Processes 111\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Chapter Focus 111\u003c\/p\u003e \u003cp\u003e4.2 Random Variables Processes and Sequences 112\u003c\/p\u003e \u003cp\u003e4.3 Statistical Properties 114\u003c\/p\u003e \u003cp\u003e4.4 Linear Random Process Models 124\u003c\/p\u003e \u003cp\u003e4.5 Shaping Filters (SF) and State Augmentation 131\u003c\/p\u003e \u003cp\u003e4.6 Mean and Covariance Propagation 135\u003c\/p\u003e \u003cp\u003e4.7 Relationships Between Model Parameters 145\u003c\/p\u003e \u003cp\u003e4.8 Orthogonality Principle 153\u003c\/p\u003e \u003cp\u003e4.9 Summary 157\u003c\/p\u003e \u003cp\u003eProblems 159\u003c\/p\u003e \u003cp\u003eReferences 167\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Linear Optimal Filters and Predictors 169\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Chapter Focus 169\u003c\/p\u003e \u003cp\u003e5.2 Kalman Filter 172\u003c\/p\u003e \u003cp\u003e5.3 Kalman–Bucy Filter 197\u003c\/p\u003e \u003cp\u003e5.4 Optimal Linear Predictors 200\u003c\/p\u003e \u003cp\u003e5.5 Correlated Noise Sources 200\u003c\/p\u003e \u003cp\u003e5.6 Relationships Between Kalman and Wiener Filters 201\u003c\/p\u003e \u003cp\u003e5.7 Quadratic Loss Functions 202\u003c\/p\u003e \u003cp\u003e5.8 Matrix Riccati Differential Equation 204\u003c\/p\u003e \u003cp\u003e5.9 Matrix Riccati Equation in Discrete Time 219\u003c\/p\u003e \u003cp\u003e5.10 Model Equations for Transformed State Variables 223\u003c\/p\u003e \u003cp\u003e5.11 Sample Applications 224\u003c\/p\u003e \u003cp\u003e5.12 Summary 228\u003c\/p\u003e \u003cp\u003eProblems 232\u003c\/p\u003e \u003cp\u003eReferences 235\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Optimal Smoothers 239\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Chapter Focus 239\u003c\/p\u003e \u003cp\u003e6.2 Fixed-Interval Smoothing 244\u003c\/p\u003e \u003cp\u003e6.3 Fixed-Lag Smoothing 256\u003c\/p\u003e \u003cp\u003e6.4 Fixed-Point Smoothing 268\u003c\/p\u003e \u003cp\u003e6.5 Summary 275\u003c\/p\u003e \u003cp\u003eProblems 276\u003c\/p\u003e \u003cp\u003eReferences 278\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Implementation Methods 281\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Chapter Focus 281\u003c\/p\u003e \u003cp\u003e7.2 Computer Roundoff 283\u003c\/p\u003e \u003cp\u003e7.3 Effects of Roundoff Errors on Kalman Filters 288\u003c\/p\u003e \u003cp\u003e7.4 Factorization Methods for “Square-Root” Filtering 294\u003c\/p\u003e \u003cp\u003e7.5 “Square-Root” and \u003ci\u003eUD\u003c\/i\u003e Filters 318\u003c\/p\u003e \u003cp\u003e7.6 \u003ci\u003eSigmaRho\u003c\/i\u003e Filtering 330\u003c\/p\u003e \u003cp\u003e7.7 Other Implementation Methods 346\u003c\/p\u003e \u003cp\u003e7.8 Summary 358\u003c\/p\u003e \u003cp\u003eProblems 360\u003c\/p\u003e \u003cp\u003eReferences 363\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Nonlinear Approximations 367\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Chapter Focus 367\u003c\/p\u003e \u003cp\u003e8.2 The Affine Kalman Filter 370\u003c\/p\u003e \u003cp\u003e8.3 Linear Approximations of Nonlinear Models 372\u003c\/p\u003e \u003cp\u003e8.4 Sample-and-Propagate Methods 398\u003c\/p\u003e \u003cp\u003e8.5 Unscented Kalman Filters (UKF) 404\u003c\/p\u003e \u003cp\u003e8.6 Truly Nonlinear Estimation 417\u003c\/p\u003e \u003cp\u003e8.7 Summary 419\u003c\/p\u003e \u003cp\u003eProblems 420\u003c\/p\u003e \u003cp\u003eReferences 423\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Practical Considerations 427\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Chapter Focus 427\u003c\/p\u003e \u003cp\u003e9.2 Diagnostic Statistics and Heuristics 428\u003c\/p\u003e \u003cp\u003e9.3 Prefiltering and Data Rejection Methods 457\u003c\/p\u003e \u003cp\u003e9.4 Stability of Kalman Filters 460\u003c\/p\u003e \u003cp\u003e9.5 Suboptimal and Reduced-Order Filters 461\u003c\/p\u003e \u003cp\u003e9.6 Schmidt–Kalman Filtering 471\u003c\/p\u003e \u003cp\u003e9.7 Memory Throughput and Wordlength Requirements 478\u003c\/p\u003e \u003cp\u003e9.8 Ways to Reduce Computational Requirements 486\u003c\/p\u003e \u003cp\u003e9.9 Error Budgets and Sensitivity Analysis 491\u003c\/p\u003e \u003cp\u003e9.10 Optimizing Measurement Selection Policies 495\u003c\/p\u003e \u003cp\u003e9.11 Summary 501\u003c\/p\u003e \u003cp\u003eProblems 501\u003c\/p\u003e \u003cp\u003eReferences 502\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Applications to Navigation 503\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Chapter Focus 503\u003c\/p\u003e \u003cp\u003e10.2 Navigation Overview 504\u003c\/p\u003e \u003cp\u003e10.3 Global Navigation Satellite Systems (GNSS) 510\u003c\/p\u003e \u003cp\u003e10.4 Inertial Navigation Systems (INS) 544\u003c\/p\u003e \u003cp\u003e10.5 GNSS\/INS Integration 578\u003c\/p\u003e \u003cp\u003e10.6 Summary 588\u003c\/p\u003e \u003cp\u003eProblems 590\u003c\/p\u003e \u003cp\u003eReferences 591\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A Software 593\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Appendix Focus 593\u003c\/p\u003e \u003cp\u003eA.2 Chapter 1 Software 594\u003c\/p\u003e \u003cp\u003eA.3 Chapter 2 Software 594\u003c\/p\u003e \u003cp\u003eA.4 Chapter 3 Software 595\u003c\/p\u003e \u003cp\u003eA.5 Chapter 4 Software 595\u003c\/p\u003e \u003cp\u003eA.6 Chapter 5 Software 596\u003c\/p\u003e \u003cp\u003eA.7 Chapter 6 Software 596\u003c\/p\u003e \u003cp\u003eA.8 Chapter 7 Software 597\u003c\/p\u003e \u003cp\u003eA.9 Chapter 8 Software 598\u003c\/p\u003e \u003cp\u003eA.10 Chapter 9 Software 599\u003c\/p\u003e \u003cp\u003eA.11 Chapter 10 Software 599\u003c\/p\u003e \u003cp\u003eA.12 Other Software Sources 601\u003c\/p\u003e \u003cp\u003eReferences 603\u003c\/p\u003e \u003cp\u003eIndex 605\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default 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