# Bayesian Estimation and Tracking: A Practical Guide

John Wiley & Sons, May 29, 2012 - Mathematics - 400 pages

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 MATLABŪ 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.

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The discussion is quite organized and well presented. It helps understand the development of different types of Bayesian filtering and show how these filtering techniques are connected.

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rohit

### Contents

 List of Tables Preliminary Mathematical Concepts General Concepts of Bayesian Estimation Case Studies Preliminary Discussions The Gaussian Noise Case Multidimensional Integration The Linear Class of Kalman Filters References The Sigma Point Class The Finite Difference Kalman Filter
 The Monte Carlo Kalman Filter Summary of Gaussian Kalman Filters Performance Measures for the Family of Kalman Filters Introduction to Monte Carlo Methods Sequential Importance Sampling Particle Filters The Generalized Monte Carlo Particle Filter A Spherical Constant Velocity Model for Target Tracking Tracking a Falling Rigid Body Using Photogrammetry

 The Sigma Point Class The Unscented Kalman Filter The Sigma Point Class The Spherical Simplex Kalman Filter The Sigma Point Class The GaussHermite Kalman Filter
 Sensor Fusion using Photogrammetric and Inertial Copyright