## With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

No credit card required ## Book Description

A practical approach to estimating and tracking dynamic systems in real-worl applications

Much of the literature on performing estimation for non-Gaussian 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 non-Gaussian 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 density-weighted integrals, including linear and nonlinear Kalman filters for Gaussian-weighted integrals and particle filters for non-Gaussian cases. The author first emphasizes detailed derivations from first principles of eeach estimation method and goes on to use illustrative and detailed step-by-step 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

1. Cover
2. Title Page
3. Copyright
4. Dedication
5. Preface
6. Acknowledgments
7. List of Figures
8. List of Tables
9. Part I: Preliminaries
1. Chapter 1: Introduction
2. Chapter 2: Preliminary Mathematical Concepts
3. Chapter 3: General Concepts of Bayesian Estimation
4. Chapter 4: Case Studies: Preliminary Discussions
10. Part II: The Gaussian Assumption: A Family of Kalman Filter Estimators
1. Chapter 5: The Gaussian Noise Case: Multidimensional Integration of Gaussian-Weighted Distributions
2. Chapter 6: The Linear Class of Kalman Filters
3. Chapter 7: The Analytical Linearization Class of Kalman Filters: The Extended Kalman Filter
4. Chapter 8: The Sigma Point Class: The Finite Difference Kalman Filter
5. Chapter 9: The Sigma Point Class: The Unscented Kalman Filter
6. Chapter 10: The Sigma Point Class: The Spherical Simplex Kalman Filter
7. Chapter 11: The Sigma Point Class: The Gauss–Hermite Kalman Filter
8. Chapter 12: The Monte Carlo Kalman Filter
9. Chapter 13: Summary of Gaussian Kalman Filters
10. Chapter 14: Performance Measures for the Family of Kalman Filters
11. Part III: Monte Carlo Methods
1. Chapter 15: Introduction to Monte Carlo Methods
2. Chapter 16: Sequential Importance Sampling Particle Filters
3. Chapter 17: The Generalized Monte Carlo Particle Filter
12. Part IV: Additional Case Studies
1. Chapter 18: A Spherical Constant Velocity Model for Target Tracking in Three Dimensions
2. Chapter 19: Tracking a Falling Rigid Body Using Photogrammetry
3. Chapter 20: Sensor Fusion using Photogrammetric and Inertial Measurements
13. Index