Bayesian Estimation and Tracking: A Practical Guide

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John Wiley & Sons, May 29, 2012 - Mathematics - 400 pages
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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
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About the author (2012)

ANTON J. HAUG, PhD, is member of the technical staff at the Applied Physics Laboratory at The Johns Hopkins University, where he develops advanced target tracking methods in support of the Air and Missile Defense Department. Throughout his career, Dr. Haug has worked across diverse areas such as target tracking; signal and array processing and processor design; active and passive radar and sonar design; digital communications and coding theory; and time- frequency analysis.

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