Book description
A practical approach to estimating and tracking dynamic systems in realworl applications
Much of the literature on performing estimation for nonGaussian systems is short on practical methodology, while Gaussian methods often lack a cohesive derivation. Bayesian Estimation and Tracking addresses the gap in the field on both accounts, providing readers with a comprehensive overview of methods for estimating both linear and nonlinear dynamic systems driven by Gaussian and nonGaussian noices.
Featuring a unified approach to Bayesian estimation and tracking, the book emphasizes the derivation of all tracking algorithms within a Bayesian framework and describes effective numerical methods for evaluating densityweighted integrals, including linear and nonlinear Kalman filters for Gaussianweighted integrals and particle filters for nonGaussian cases. The author first emphasizes detailed derivations from first principles of eeach estimation method and goes on to use illustrative and detailed stepbystep instructions for each method that makes coding of the tracking filter simple and easy to understand.
Case studies are employed to showcase applications of the discussed topics. In addition, the book supplies block diagrams for each algorithm, allowing readers to develop their own MATLAB toolbox of estimation methods.
Bayesian Estimation and Tracking is an excellent book for courses on estimation and tracking methods at the graduate level. The book also serves as a valuable reference for research scientists, mathematicians, and engineers seeking a deeper understanding of the topics.
Table of contents
 Cover
 Title Page
 Copyright
 Dedication
 Preface
 Acknowledgments
 List of Figures
 List of Tables
 Part I: Preliminaries

Part II: The Gaussian Assumption: A Family of Kalman Filter Estimators
 Chapter 5: The Gaussian Noise Case: Multidimensional Integration of GaussianWeighted Distributions
 Chapter 6: The Linear Class of Kalman Filters
 Chapter 7: The Analytical Linearization Class of Kalman Filters: The Extended Kalman Filter
 Chapter 8: The Sigma Point Class: The Finite Difference Kalman Filter
 Chapter 9: The Sigma Point Class: The Unscented Kalman Filter

Chapter 10: The Sigma Point Class: The Spherical Simplex Kalman Filter
 10.1 OneDimensional Spherical Simplex Sigma Points
 10.2 TwoDimensional Spherical Simplex Sigma Points
 10.3 Higher Dimensional Spherical Simplex Sigma Points
 10.4 The Spherical Simplex Kalman Filter
 10.5 The Spherical Simplex Kalman Filter Process
 10.6 Application of the SSKF to the DIFAR Ship Tracking Case Study
 References
 Chapter 11: The Sigma Point Class: The Gauss–Hermite Kalman Filter
 Chapter 12: The Monte Carlo Kalman Filter
 Chapter 13: Summary of Gaussian Kalman Filters
 Chapter 14: Performance Measures for the Family of Kalman Filters

Part III: Monte Carlo Methods
 Chapter 15: Introduction to Monte Carlo Methods

Chapter 16: Sequential Importance Sampling Particle Filters
 16.1 General Concept of Sequential Importance Sampling
 16.2 Resampling and Regularization (Move) for SIS Particle Filters
 16.3 The Bootstrap Particle Filter
 16.4 The Optimal SIS Particle Filter
 16.5 The SIS Auxiliary Particle Filter
 16.6 Approximations to the SIS Auxiliary Particle Filter
 16.7 Reducing the Computational Load Through RaoBlackwellization
 References
 Chapter 17: The Generalized Monte Carlo Particle Filter

Part IV: Additional Case Studies

Chapter 18: A Spherical Constant Velocity Model for Target Tracking in Three Dimensions
 18.1 Tracking a Target in Cartesian Coordinates
 18.2 Tracking a Target in Spherical Coordinates
 18.3 Implementation of Cartesian and Spherical Tracking Filters
 18.4 Performance Comparison for Various Estimation Methods
 18.5 Some Observations and Future Considerations
 Appendix 18.A ThreeDimensional Constant Turn Rate Kinematics
 Appendix 18.B ThreeDimensional Coordinate Transformations
 References
 Chapter 19: Tracking a Falling Rigid Body Using Photogrammetry
 Chapter 20: Sensor Fusion using Photogrammetric and Inertial Measurements

Chapter 18: A Spherical Constant Velocity Model for Target Tracking in Three Dimensions
 Index
Product information
 Title: Bayesian Estimation and Tracking: A Practical Guide
 Author(s):
 Release date: June 2012
 Publisher(s): Wiley
 ISBN: 9780470621707
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