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Kalman Filtering Theory and Practice with MATLAB Theory and Practice with MATLAB 4th Edition by Mohinder S Grewal ISBN 1118851218 9781118851210

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Kalman Filtering Theory and Practice Using Matlab Fourth edition Mohinder S. Grewal Digital Instant Download

Author(s): MOHINDER S. GREWAL ,ANGUS P. ANDREWS
ISBN(s): 9781118851210, 1118851218
File Details: PDF, 5.09 MB
Year: 2015
Language: english
SKU: EB-5089530 Category: Tags: ,

Kalman Filtering Theory and Practice with MATLAB Theory and Practice with MATLAB 4th Edition by Mohinder S Grewal – Ebook PDF Instant Download/Delivery: 1118851218, 9781118851210
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ISBN 10: 1118851218 
ISBN 13: 9781118851210
Author: Mohinder S Grewal

The definitive textbook and professional reference on Kalman Filtering – fully updated, revised, and expanded This 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. Kalman Filtering: Theory and Practice Using MATLAB, Fourth Edition 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 important topic.

Kalman Filtering Theory and Practice with MATLAB Theory and Practice with MATLAB 4th Table of contents:

Chapter 1: Introduction
1.1 Chapter Focus
1.2 On Kalman Filtering
1.3 On Optimal Estimation Methods
1.4 Common Notation
1.5 Summary
Problems
References

Chapter 2: Linear Dynamic Systems
2.1 Chapter Focus
2.2 Deterministic Dynamic System Models
2.3 Continuous Linear Systems and their Solutions
2.4 Discrete Linear Systems and their Solutions
2.5 Observability of Linear Dynamic System Models
2.6 Summary
Problems
References

Chapter 3: Probability and Expectancy
3.1 Chapter Focus
3.2 Foundations of Probability Theory
3.3 Expectancy
3.4 Least-Mean-Square Estimate (LMSE)
3.5 Transformations of Variates
3.6 The Matrix Trace in Statistics
3.7 Summary
Problems
References

Chapter 4: Random Processes
4.1 Chapter Focus
4.2 Random Variables, Processes, and Sequences
4.3 Statistical Properties
4.4 Linear Random Process Models
4.5 Shaping Filters (SF) and State Augmentation
4.6 Mean and Covariance Propagation
4.7 Relationships Between Model Parameters
4.8 Orthogonality Principle
4.9 Summary
Problems
References

Chapter 5: Linear Optimal Filters and Predictors
5.1 Chapter Focus
5.2 Kalman Filter
5.3 Kalman–Bucy Filter
5.4 Optimal Linear Predictors
5.5 Correlated Noise Sources
5.6 Relationships Between Kalman and Wiener Filters
5.7 Quadratic Loss Functions
5.8 Matrix Riccati Differential Equation
5.9 Matrix Riccati Equation in Discrete Time
5.10 Model Equations for Transformed State Variables
5.11 Sample Applications
5.12 Summary
Problems
References

Chapter 6: Optimal Smoothers
6.1 Chapter Focus
6.2 Fixed-Interval Smoothing
6.3 Fixed-Lag Smoothing
6.4 Fixed-Point Smoothing
6.5 Summary
Problems
References

Chapter 7: Implementation Methods
7.1 Chapter Focus
7.2 Computer Roundoff
7.3 Effects of Roundoff Errors on Kalman Filters
7.4 Factorization Methods for “Square-Root” Filtering
7.5 “Square-Root” and UD Filters
7.6 SigmaRho Filtering
7.7 Other Implementation Methods
7.8 Summary
Problems
References

Chapter 8: Nonlinear Approximations
8.1 Chapter Focus
8.2 The Affine Kalman Filter
8.3 Linear Approximations of Nonlinear Models
8.4 Sample-And-Propagate Methods
8.5 Unscented Kalman Filters (UKF)
8.6 Truly Nonlinear Estimation
8.7 Summary
Problems
References

Chapter 9: Practical Considerations
9.1 Chapter Focus
9.2 Diagnostic Statistics and Heuristics
9.3 Prefiltering and Data Rejection Methods
9.4 Stability of Kalman Filters
9.5 Suboptimal and Reduced-Order Filters
9.6 Schmidt–Kalman Filtering
9.7 Memory, Throughput, and Wordlength Requirements
9.8 Ways to Reduce Computational Requirements
9.11 Summary
Problems
References

Chapter 10: Applications to Navigation
10.1 Chapter Focus
10.2 Navigation Overview
10.3 Global Navigation Satellite Systems (GNSS)
10.4 Inertial Navigation Systems (INS)
10.5 GNSS/INS Integration
10.6 Summary

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Tags: Mohinder S Grewal, Kalman Filtering, MATLAB